Automatic analyte sensor calibration and error detection

ABSTRACT

Systems and methods are provided that address the need to frequently calibrate analyte sensors, according to implementation. In more detail, systems and methods provide a preconnected analyte sensor system that physically combines an analyte sensor to measurement electronics during the manufacturing phase of the sensor and in some cases in subsequent life phases of the sensor, so as to allow an improved recognition of sensor environment over time to improve subsequent calibration of the sensor.

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 16/402,013, filed May 2, 2019, which claims the benefit of U.S.Provisional Application No. 62/666,606, filed May 3, 2018. Each of theaforementioned applications is incorporated by reference herein in itsentirety, and each is hereby expressly made a part of thisspecification.

TECHNICAL FIELD

The embodiments described herein relate generally to systems and methodsfor processing sensor data from continuous analyte sensors and forself-calibration.

BACKGROUND

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin-dependent) and/or in which insulinis not effective (Type II or non-insulin-dependent). In the diabeticstate, the patient or user suffers from high blood sugar, which cancause an array of physiological derangements associated with thedeterioration of small blood vessels, for example, kidney failure, skinulcers, or bleeding into the vitreous of the eye. A hypoglycemicreaction (low blood sugar) can be induced by an inadvertent overdose ofinsulin, or after a normal dose of insulin or glucose-lowering agentaccompanied by extraordinary exercise or insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a personwith diabetes normally only measures his or her glucose levels two tofour times per day. Unfortunately, such time intervals are so far spreadapart that the person with diabetes likely finds out too late of ahyperglycemic or hypoglycemic condition, sometimes incurring dangerousside effects. It is not only unlikely that a person with diabetes willbecome aware of a dangerous condition in time to counteract it, but itis also likely that he or she will not know whether his or her bloodglucose concentration value is going up (higher) or down (lower) basedon conventional methods. Diabetics thus may be inhibited from makingeducated insulin therapy decisions.

Another device that some diabetics used to monitor their blood glucoseis a continuous analyte sensor, e.g., a continuous glucose monitor(CGM). A CGM typically includes a sensor that is placed invasively,minimally invasively or non-invasively. The sensor measures theconcentration of a given analyte within the body, e.g., glucose, andgenerates a raw signal using electronics associated with the sensor. Theraw signal is converted into an output value that is rendered on adisplay. The output value that results from the conversion of the rawsignal is typically expressed in a form that provides the user withmeaningful information, and in which form users have become familiarwith analyzing, such as blood glucose expressed in mg/dL.

The above discussion assumes the output value is reliable and true, andthe same generally requires a significant degree of user interaction toensure proper calibration. Typically, a calibration check is performedbefore the analyte sensor leaves the factory; during the calibrationcheck, sensitivity values are derived in vitro. However, the calibrationcheck only provides a snapshot of the sensitivity at a given point intime and does not take into account that sensor sensitivity changes overtime. Moreover, two sensors that have the same result from thecalibration check procedure can still act differently in use in apatient, as the values of sensitivity can diverge over time depending onconditions before and after use.

One way of accounting for this is by use of reference value checksduring use, e.g., by self monitoring blood glucose meters. Many currentCGMs rely heavily on such user interactions, confirming glucoseconcentration values before dosing insulin. However, additional useraction adds a significant source of error in the monitoring and reducesconvenience by requiring more action of the user than desired.

SUMMARY OF THE INVENTION

Systems and methods according to present principles address many of theissues above concerning the need to frequently calibrate analytesensors, according to implementation. In more detail, systems andmethods provide a preconnected analyte sensor system that physicallycombines an analyte sensor to measurement electronics during themanufacturing phase of the sensor and in some cases in subsequent lifephases of the sensor.

In one embodiment, at a minimum the system includes an analyte sensorcapable of measuring an analyte level in a host and measurementelectronics containing a potentiostat circuit capable of placing acontrolled voltage bias between two or more electrodes and measuring theamount of current that flows. The analyte sensor is preconnected to themeasurement electronics.

There are also several optional features: a sensor interconnectionmodule capable of securing an analyte sensor in position and/or robustelectrical coupling, and a measurement electronics module which mayinclude one or more of the following: a temperature measurement circuitcapable of taking temperature readings from one or more temperaturesensors, an impedance measurement circuit capable of detecting impedancevalues from the analyte sensor or other electrical components, acapacitive measurement circuit capable of detecting capacitance valuesfrom the analyte sensor or other electrical components, a motiondetecting circuit using one or more sensors such as an accelerometer orgyroscope to detecting and quantifying physical motion and/ororientation, a humidity measurement circuit with one or more sensorsable to measure humidity, a clock capable of keeping a measure of time,and/or a pressure measurement circuit with one or more pressure sensorscapable of measuring pressure of a gas (e.g., barometric pressure) orchanges in pressure applied to the device (e.g., force applied to asurface of a housing), and one or more processors capable of processingdata. Other features may include one or more radios capable ofwirelessly transmitting data, one or more display/status indicatorscapable of communicating data to a user, one or more data storage unitscapable of storing relevant information for future access, and one ormore power sources (e.g., a battery) capable of delivering reliablepower for use by the measurement electronics.

A preconnected analyte sensor can address various sources of error thatmay otherwise arise. These sources of error may involve both errors inaccuracy and precision, which are key factors in determining the truevalue of a measurement performed by a measurement system. Accuracy canbe described as the closeness of a measured value to a standard or knownvalue. For example, when taking a width measurement of a known 1 cm cubeand a value obtained is 1.1 cm, the measurement is accurate to 0.1 cm.Precision is the degree to which repeated measurements under unchangedconditions show the same results. In the same cube example if threemeasurements are taken and the values obtained are 1.1 cm, 1.2 cm, and1.0 cm the measurement is precise to within 0.1 cm. However, precisionand accuracy error are compounded in the determination of the truenessof a measurement.

Precision and accuracy are not static factors that can impact errors ina measurement system. Rather, they are dynamic factors in whichprecision and accuracy can vary over time. Typically a model is used(e.g. linear, non-linear, etc.) to quantify a sensor response to signal,and so deviations to the precision and accuracy of the model used toquantify a sensor response add additional error in the conversion of asensor signal to a reported value.

Therefore, preconnecting an analyte sensor system to measurementelectronics in the manufacturing phase and then using the sameconfiguration during the sensor use phase has several advantages.

The preconnected analyte sensor system can compensate for errorsintroduced by the accuracy and precision of manufacturing equipment.Variations in the manufacturing process may give rise to differentvalues for various parameters that are measured (e.g., analytesensitivity, baseline, impedance, capacitance, interferent sensitivity,etc.), and the errors resulting from these different parameter valuesare compounded into the error of the overall system. The more variationsthere are in the manufacturing setup, the more significant theconsequences to the error introduced in the system. These variations mayinclude: changes to equipment over time, frequency of equipmentcalibration, number of different measurement stations, multiplemanufacturing lines, multiple manufacturing locations, equipmentprecision, calibration trueness, equipment cleanliness, etc.

The preconnected analyte sensor system limits error caused by thephysical connection of the analyte sensor to the electronics portion ofthe sensor, and where the electronics portion includes measurementelectronics, allows measurements to be taken during and after themanufacturing process. Several of the possible measurement types thatcan be taken by measurement electronics are sensitive to factors suchas: contact resistance, leakage current, length of electrical pathways,component volume, manufacturing tolerances, material properties, etc.

The preconnected analyte sensor system limits error introduced by themeasurement electronics. Measurement electronics are limited by theirown manufacturing tolerances and their design limitations. Typically,calibration equipment is used to characterize a measurement electronicsystem. The accuracy and precision are measured and correction factors(e.g., gain, offset, linearity, temperature, resolution, etc.) are usedby the circuit to compensate for absolute error. This adds cost andcomplexity to the manufacturing phase as testing time and programmingtime must be added to the process. Also, depending on the time periodand the equipment used to calibrate the system, changes in variousproperties may arise from the time of calibration. It is thereforeadvantageous to calibrate the system as late in the manufacturingprocess as possible.

In manufacturing, having fewer steps in the process has advantages forefficiency and reducing opportunity for error. By performing a sensorcalibration using the measurement electronics that will be used in thefinal product, calibration can be accomplished as a system. For example,to calibrate the electronics and sensor as a single step in a knowncalibration solution, only the value of the calibration solution must becontrolled. The measurement electronics at minimum are placing a voltagebias on the sensor, measuring an analog value of current, and convertingthat analog value to a digital value. This digital value can becorrelated to the actual value of the calibration solution. For thisparticular set of measurement electronics in combination with thisparticular sensor, the relationship between an analyte concentration ina calibration solution is now linked to a digital value that iscorrected for individual measurement component variation (e.g.potentiostat variability, analog to digital converter error, leakageerror, connection resistance variability, etc.). This system alsoeliminates manufacturing measurement electronic error from calibrationequipment.

This direct-to-calibration solution type of system calibration can beperformed over a broad range of analyte values, interferent materials,and other factors that affect sensor performance (e.g., low oxygen).This correlation of digital values to analyte concentration in asolution over a range can be used to build an accurate compensationmodel for in-vivo sensor performance.

In an alternative embodiment this process of calibration can be extendedto other types of possible measurements performed by measurementelectronics (e.g., impedance, capacitance, temperature, time, current,voltage, humidity, motion, etc.).

The value of a system that connects measurement electronics to ananalyte sensor during manufacture can be extended beyond the calibrationportion of manufacturing. This enables the system to capture data duringthe following system phases: manufacturing, packaging, sterilization,shipping, storage, insertion, and in vivo. Useful measurements can betaken before, during, or after one or more of the following steps:sensor connection, membrane application, curing, environmentalexcursions, sterilization, shipping, storage, insertion, in vivo, etc.

In transcutaneous analyte measurement systems that are currentlyavailable on the market, the sensor and measurement electronics arecoupled immediately prior or during sensor insertion. This configurationprevents measurements of a coupled system during any system phase priorto the measurement electronic and analyte sensor coupling. Theadditional measurements that are only capable of being captured with apreconnected system can be provided to an analyte processing algorithm.These measurements can be correlated to in vivo performance, faultdetection, sensor life, sensitivity shift, calibration shift, sensorperformance indicator, accuracy, etc. The measurement correlations canbe used to identify or compensate for system experience over an extendedtime period that is useful during the in vivo system phase.

For multiple measurements at different time points and system phases amulti variate model can be created. This frequency and breadth of datagathering can more accurately model system characteristics. Some of thisanalysis can be accomplished using measurements taken by manufacturingor calibration equipment. These input measurements may optionally beincorporated in addition to measurements taken by measurementelectronics. In other embodiments the model may only include input frommanufacturing and/or calibration equipment. The output measurements maybe taken by manufacturing and/or calibration equipment or during the invivo phase by reference measurements of blood analyte levels (e.g. YSI,finger stick blood glucose meters, laboratory analysis, etc.).

For example, measurements such as impedance, temperature, currentmeasurements, time, etc. may be taken by preconnected measurementelectronics during various phases of manufacture such as pre-sensorattachment, post-sensor attachment, membrane application, curing, andcalibration. The preconnected system may collect spatial informationsuch as location in a fixture, location in equipment, or an equipmentidentifier. This data set may be combined with an additional data setfrom sensors placed in manufacturing equipment that gather variablessuch as humidity, temperature, material viscosity, time, equipmentidentifier, etc. An additional data set can also be gathered that trackexternal variables such as time, date, room temperature, room humidity,manufacturing equipment, calibration equipment, operator, manufacturingline, manufacturing location, etc.

The collated measurements can be interpreted immediately or stored forfurther processing at a later time. The information can be used toadjust manufacturing parameters or to build a correction factor,determine lot classification, reject sensors, or used by an analyteprocessing algorithm. This large amount of data can be input into toolssuch as machine learning algorithms to identify correlations.

The multi variable model can be used to identify and correct forrelationships between input parameters and output parameters. Some ofthese relationships are well known (e.g. the relationship of temperatureon analyte sensitivity measurements) and others have yet to beidentified. Tools used to identify and model these relationships may be:linear regression additive models, generalized linear modelingoptionally incorporating one or more nonlinear functions, non-parametricdata fitting to empirical modeling, nonlinear regression modeling,neural network models, or other suitable models. This list is onlyexemplary and any suitable statistical or analysis tools can be used tomodel system relationships. Other suitable methods of data analysis aredescribed in “Handbook of Chemometrics and Qualimetrics, Volume 20A” and“Handbook of Chemometrics and Qualimetrics, Volume 20B” published byElsevier Science 1998 and incorporated by reference.

Many system measurements that can be taken have known correlations toadditional system parameters. In this way it is possible to drawcorrelations to parameters that are not directly measured but which maybe useful to input or process with an analyte algorithm processing unit.This has several advantages such as requiring less physical sensorcomponents that add cost and complexity, gathering information that isnot easily measured due to location or sensor size, providing redundancyor improved accuracy to additional sensors (e.g. compensating fortemperature in a current measuring circuit).

Example applications utilizing inferred measurements may be some of thefollowing: using temperature and sensor impedance measurements to inferhumidity levels ex vivo; using one or more temperature sensors tocalculate a temperature gradient; using the temperature gradient data toestimate temperature of a non-measured point such as the tip of ananalyte sensor in vivo; using temperature and accelerometer data toestimate physical exertion. This is not a complete list and any of thesensed measurements can be combined with one or more other sensedmeasurements to estimate one or more non-sensed measurements.

By pre-connecting the sensor to some or all of the sensor electronics,the sensor can be monitored throughout all or part of its life, and mostespecially during the part of the sensor's life after it leaves thefactory. Sensor monitoring may be advantageous for a number of reasons.In particular, it can address issues concerning variability (thedivergence over time from a sensor's calibration value assigned in thefactory), accuracy (the error added to the overall analyte sensor systemarising from variability in the individual components that make up thesystem) and manufacturing processes that reduce consistency from sensorto sensor and sensor lot to sensor lot. Additionally, a preconnectedsensor can facilitate data transfer from the sensor to external devicesand provide improvements to sensor safety by detecting when a sensordeployed in the field is potentially unsafe.

In one aspect, variability issues are addressed by performing variousactive measurements that are taken post-manufacturing. For instance, inone embodiment, environmental conditions (e.g., temperature, humidity)under which the sensor and preconnected electronics are maintained whilesealed in packaging during storage and prior to use may be monitored. Inthe case of temperature, an on-board electronics temperature sensor suchas a thermistor or thermocouple may be used to measure and storetemperature data. Likewise, an on-board electronics humidity sensor maybe provided to monitor humidity. Alternatively, an external temperatureand/or humidity sensor physically coupled to the electronics (e.g., inthe base, in the package) may be used to measure and store temperatureand/or humidity data. In other cases an independent temperature and/orhumidity sensor that is in wireless communication with the electronicsmay be used. In some cases there may be an individual temperature and/orhumidity sensor assigned to each analyte sensor. Alternatively, theremay be a single temperature and/or or humidity sensor assigned to eachbox/shipper/pallet of analyte sensors. In another implementation theanalyte sensor wire itself may be used to determine temperature and/orhumidity by inference via impedance or current measurements, whichmeasurements may be stored in the preconnected electronics.

In some embodiments another environmental condition that may bemonitored is the radiation dose that is imparted to the sensor forsterilization purposes after the sensor and any preconnected electronicshave been sealed in packaging. In one example a sterilization detectormay be provided on the electronics so that the detector is able toquantify the dose amount using the active electronics. In some casesmaterial may be added to the packaging that is sensitive to thesterilization dose and which can be electronically interrogated by theelectronics post-sterilization to determine sensor characteristics suchas impedance, resistance and/or capacitance. From this it may bepossible to infer the orientation of the device in the packaging duringsterilization. Bulk detection of the sterilization dose may also beobtained for each box/shipper/pallet of analyte sensors. The dosage thatis measured may be used to assign a value to the analyte sensor viawireless communication with the preconnected electronics, the value forlater use in deriving subsequent calibration parameters.

In an additional aspect, another environmental condition that may bemonitored is movement of the analyte sensor using an accelerometer, atriggering break fuse or other motion sensor. In this way vibrations orimpact due to dropping or the like may be detected, which can causedamage to the sensor membrane or applicator mechanism.

Yet other environmental conditions that may be monitored include ambientgas exposure and the duration of time that has elapsed since sensormanufacture.

In addition to or instead of the active monitoring techniques discussedabove to address variability issues, passive techniques may also beused. For instance, in one implementation, described in U.S. ApplicationNo. 62/521,969, filed Jun. 19, 2017, entitled “Applicators for ApplyingTranscutaneous Analyte Sensors and Associated Methods of Manufacture,the packaging material that is used may provide a humidity barrier thatcan maintain the moisture vapor transmission rate below some thresholdlevel, e.g., less than 10 grams/100 in²/day or less than 1 grams/100in²/day. Examples of packaging material that may be used includemetallic foil (e.g. aluminum, titanium), a metallic substrate, aluminumoxide coated polymer, silicon dioxide coated polymer, a polymersubstrate coated with a metal applied via vapor metallization, or lowMVTR polymers (e.g. PET, HDPE, PVC, PP, PLA).

Yet another passive technique that may be used to monitor environmentalconditions, includes the provision of a visual indicator material in thepackaging which changes color or visibility with exposure to temperatureand/or humidity over time. Alternatively, instead of a visual indicator,the indicator may undergo a dimensional change in length or position inresponse to temperature or humidity changes.

In some embodiments that employ humidity and/or temperature monitoringin the packaging, if either or both such monitors determine that theenvironmental conditions have, at some point, for some duration,exceeded acceptable limits, the packaging may be provided with amechanism to physically prevent the sensor in that packaging from beingused. For instance, a material that changes in dimensions withtemperature and/or humidity such as a bimetal (similar to those used inthermostats), metal, or polymer may be used in combination with aninterlocking feature in the applicator to physically (either permanentlyor temporarily) prevent the applicator from deploying, preventing thepackaging from being opened, and/or preventing a button or the like frombeing activated. The physical change in the material dimensions willautomatically enable this feature when the predetermined environmentalconditions are exceeded.

In another aspect, system level compensation may be achieved whichallows for greater parameter variability among individual systemcomponents while reducing overall error. This may be accomplished usingthe data stored in the preconnected electronics concerning the monitoredenvironmental conditions as input to an algorithm that is used to adjustthe sensor calibration model. The adjustments may be made to the initialand/or final sensor sensitivity, the background signal and/or theequilibration rate. In some cases, the data that is gathered and storedfor an individual sensor or a sensor lot may be tailored to anindividual patient. Moreover, the adjustments that are needed may alsouse as an additional input information that has been previously obtainedover time for large numbers of sensors and patients to calculatecalibration compensation values based on the performance of sensors thathad experienced similar conditions.

The algorithm that is used to adjust the sensor calibration model mayalso include a time component that uses data obtained by examining thesensitivity profile and background signal profile of the sensor over thetime from insertion (when the factory calibrated initial sensitivity andbackground signal is used) to the transition to a stable finalsensitivity and background signal. The sensor calibration model may becompensated based on the difference between the factory calibrationvalue and the rate of change during the sensitivity transition period.Typical break-in curves can be obtained for sensors from this data aswell as changes to the curves arising from changes induced bysterilization, temperature, humidity and/or storage time. These break-incurves may be used to compensate the sensor calibration model fordeviations from the factory calibration.

In another aspect, the sensor calibration model that is updated based onthe data stored in the preconnected electronics concerning the monitoredenvironmental conditions may be used to make adjustments to the sensorcalibration value prior to insertion of the analyte sensor in thepatient. For example, the voltage bias applied to the analyte sensor maybe adjusted based on the stored data. In some cases the voltage bias maybe applied while the analyte sensor is in its packaging to change thesensor properties in order to, for example, have the sensor undergobreak-in while in the packaging. In addition, the packaging may containa calibration solution that may be embedded in a foam, gel, etc., toprevent spillage. The calibration solution can be released shortlybefore the package is opened or while the package is being opened tofacilitate calibration of the sensor in the package. In yet anotheraspect, the estimated break-in time that the sensor needs prior to startup may be adjusted based on the stored data, including the age of sensorand its measured impedance. The break-in time estimated in this mannermay be displayed on the display of the system.

In another aspect the stored data may be used in conjunction withmeasurements obtained in vivo to adjust for sensitivity shifts thatarise in vivo. For instance, the impedance may be measured in vivo inresponse to a stimulus signal, which may be a pulse, single frequency,multiple frequency, or spectroscopy (EIS) signal. The measured impedanceshift can be correlated to changes in sensitivity, but the correlationmay be made more complex by changes in temperature and ionicconcentration (such as sodium) in the surrounding fluid. To address thisissue, impedance measurements can be taken at one or more temperaturesin the factory and changes in temperature can be mapped to shifts in theimpedance measurement. This information can then be used in vivo bytaking a temperature measurement in vivo and making any adjustments tothe relationship between the measured impedance shift and changes insensitivity. Likewise, impedance measurements can be taken at one ormore ionic concentrations in the factory and changes in concentrationcan be mapped to shifts in the impedance measurement. This informationcan then be used in vivo by taking an ionic concentration measurement invivo and making any adjustments to the relationship between the measuredimpedance shift and changes in sensitivity. The ionic concentration maybe measured using a secondary electrode circuit that may be located onthe same body as the analyte measurement circuit or on anothersubcutaneous sensor body. In some cases the ionic concentration may beobtained by optical measurements via changes to the refractive index ofthe fluid. The light source for such optical measurements may be ambientlight or a dedicated light source that exposes the fluid to light of aknown wavelength.

The accuracy of a preconnected sensor depends in part on the error addedto the system in the factory by combining components with individualvariability. Such errors that can impact the system level calibrationmay arise from the sensor sensitivity (e.g., the slope, baseline andO₂), membrane defects (e.g., impedance detection), electronics (e.g.,voltage bias accuracy, current measurement linearity, leakage current),the calibration process (e.g., solution accuracy, measurement equipmentaccuracy) and the interconnect coupling the analyte sensor and theelectronics (e.g., the resistance value and variations between theanalyte sensor and measurement electronics, and between the analytesensor and the calibration electronics).

In another aspect, pre-connecting the analyte sensor and the variouscomponents of the electronics may give rise to manufacturingimprovements. For instance, such a pre-connection can allow for improvedsensor tracking and serialization by providing a component attached tothe sensor that has a surface on which a code (e.g. barcode, label,etc.) can be located for use in identification. The code, which mayserve as a unique identifier, may be applied during or beforemanufacturing. The code may also include sensor data such as acalibration code, sensitivity value, etc., which are obtained duringmanufacturing. In some cases wireless communication may be establishedwith the preconnected sensor during the manufacturing process. Forexample, the sensors can be identified and tracked via wirelessinterrogation using short-range wireless communication protocols such asRFID, NFC and Bluetooth. Likewise, the analyte sensor can activelybroadcast data or its identifier using a short range wirelesscommunication protocol. In this way the handling efficiency of theanalyte sensor during manufacturing can be improved as the sensors aremoved, connected and disconnected multiple times. The body of thepreconnected electronics can also serve as an anchoring body forconnection and alignment that may improve the manufacturing flow.Further improvements can arise from replacing physical electricalconnections with non-contact wireless methods.

In another aspect, the calibration code affixed to the sensor,transmitter, packaging or other component may be a dynamic calibrationcode that changes with changes in environmental conditions. For example,portions of a printed code (e.g., a barcode) may be obscured byenvironmentally reactive pigments such as a thermochromatic dye, whichcause the value of the code to change. In the shipping industry,reactive pigments are employed which turn black (or some other color),or which turn from transparent to black based on exposure to heat, cold,humidity or shock (by being dropped, for example). Thus, if acalibration code were printed on the packaging, for instance, it couldcontain a base calibration code which adjusts the calibration curve fora sensor. Additional digits may be printed such that they eitherdisappear or appear when exposed to an environmental factor that impactscalibration.

For instance, in an example of a dynamic calibration code in the form ofa barcode, a predetermined digit, say “3,” may indicate heat exposure.If in this example the package is exposed to heat over a threshold valuethe digit 3 disappears, as does its corresponding portion of thebarcode. Another digit, say “7,” may indicate that humidity exposure isat a threshold. If the humidity surpasses the threshold the digit 7appears, as does its corresponding portion of the barcode. When scanned,or otherwise entered into the software within a patient's mobile deviceor other receiver, a calibration curve offset or adjustment can begenerated. Additionally, this information may be transmitted back to themanufacturer to determine lot variability as well as variability duringshipping, thereby identifying poorly stored sensors. This informationmay also flow back to accounting for inventory write down as well.Additional reactive pigments may include a “cut off” threshold which arelocated on the periphery of the code and which would appear or disappearif the sensor was exposed to something which renders it unusable. Thissame information may be used to accrue an end user credit and reshipmentas well as the aforementioned accounting write down.

In another aspect, pre-connecting the analyte sensor and the variouscomponents of the electronics may allow manufacturing improvements byusing closed loop manufacturing feedback, which can allow manufacturingvariables to be monitored in real time to modify the manufacturingprocess to improve the resulting sensors. The sensors can be in the formof a brick, fixture, or individual sensors. Variables that can bemonitored include, by way of illustration, temperature, humidity, thecontent (e.g., PVP, ethanol, etc.) of the particular coating solution inwhich the sensor is dipped (which may be determined from the refractiveindex of the solution), the duration of the dip, the number of times thesensors are dipped in the solution, and the duration, temperature andhumidity of the curing process. The data gathered during this monitoringprocess may allow large sensor data sets concerning the manufacturingprocess to be obtained, which can be used to create outcome-basedpredictors. For instance, if as a result of this process it isdetermined that at some point during the manufacturing process thetemperature was higher than its mean value, the humidity was lower thanits mean value and the sensor sensitivity was higher than its meanvalue, an update to the manufacturing process may be implemented basedon this insight to reduce deviations in the sensor sensitivity from themean value. Moreover, since the processes can be continuously monitored,it can be determined if the updates to the manufacturing processactually improve the outcome.

In addition to using data gathered about individual sensors as feedbackduring the manufacturing process, sensor lot information may be obtainedand stored. In this way additional information may be obtained that canbe used as feedback during the manufacturing process. For instance, longterm testing for shifts in e.g., the sensitivity, of sensor lots may bestored in the cloud for use in a suitable algorithm. Likewise,information concerning the sensor shipping process (geographicinformation, means of transportation used, duration of shipping process,etc.) may be obtained and stored so that it can be subsequentlycorrelated with sensor data to determine the effects of environmentalexposure.

In another aspect, in addition to using data gathered duringmanufacturing as part of a closed loop feedback process, data concerningthe sensor and the patient while the sensor is in vivo may also be used.For instance, analytics from individual sensor performance in a patientmay be used as input data into any number of algorithms used during themanufacturing process. Such data may be obtained from devices such as amobile phone or other receiver that are in communication with the sensorwhile in use. The data that is obtained may be any available informationsuch as temperature, humidity, sensor motion (which may indicate, forinstance, that the patient is sleeping, exercising, etc.), compressionalforces that can be determined from an accelerometer and which may beexerted on the sensor while the user is in different positions (e.g.,sitting, standing, laying down) and patient proximity to known locations(e.g., Wi-Fi beacons, cell towers, internet-of-things (IOT) devices).

In another aspect, the stored data obtained from the sensor during andafter manufacturing can be used to reduce the risk of potentially unsafesensors being deployed in the field. Such data may be used to examinethe efficacy of various storage conditions (e.g., packaging barriers andpackaging indictors) and sterilization conditions (by, e.g., samplingsensor lots that undergo sterilization) and to better determine when asensor is expected to expire based on its age and the available dataconcerning the manufacturing, storage and other environmental conditionsexperienced by the sensor. In this way the patient can be automaticallynotified (by e.g., an app pop-up, email, automated phone call) when asensor is expected to expire.

In a first aspect, a method is provided for self-calibration of ananalyte sensor system that includes an analyte sensor operativelycoupled to sensor electronics, comprising: applying a bias voltage withthe sensor electronics to the analyte sensor to generate sensor data,the analyte sensor system having an initial characteristic metricdetermined at a first time; using the sensor electronics at a secondtime subsequent to the first time to determine a change to the initialcharacteristic sensitivity metric of the analyte sensor system based atleast in part on one or more manufacturing and/or environmentalparameters; and using the sensor electronics to automatically calibrate,without user intervention, the analyte sensor system based at least inpart on the determined change to the initial characteristic metric.

In an embodiment of the first aspect or any other embodiment thereof,one or more environmental parameters are monitored between the firsttime and second time.

In an embodiment of the first aspect or any other embodiment monitoringthe one or more environmental parameters includes measuring an impedanceof the analyte sensor by: applying a stimulus signal to the analytesensor; measuring a signal response to the stimulus signal; calculatingthe impedance based on the signal response; and determining a value forthe environmental parameter based on an established relationship betweenthe impedance and the environmental parameter.

In an embodiment of the first aspect or any other embodiment thereof,the first time is subsequent to sensor fabrication and the second timeis prior to sensor use in vivo.

In an embodiment of the first aspect or any other embodiment thereof,the first time is subsequent to sensor fabrication and the second timeis subsequent to initiation of sensor use in vivo.

In an embodiment of the first aspect or any other embodiment thereof,the initial characteristic metric is determined by initially calibratingthe analyte sensor while the analyte sensor is operatively coupled to asensor interface that is configured to provide an electricalcommunication interface between the analyte sensor and each of amanufacturing station and the sensor electronics.

In an embodiment of the first aspect or any other embodiment thereof,the initial characteristic metric is further determined by measuring anin vitro sensitivity characteristics of the analyte sensor.

In an embodiment of the first aspect or any other embodiment thereof,the initial characteristic metric is determined by initially calibratingthe analyte sensor while the analyte sensor is operatively coupled toone or more components of the sensor electronics.

In an embodiment of the first aspect or any other embodiment thereof,the one or more components includes a potentiostat.

In an embodiment of the first aspect or any other embodiment thereof,the analyte sensor is continuously operatively coupled to the one ormore components of the sensor electronics between the first and secondtimes without interruption.

In an embodiment of the first aspect or any other embodiment thereof,the first time is during a first portion of a manufacturing life phaseof the analyte sensor and the second time is during a second portion ofthe manufacturing life phase that is subsequent to packaging the analytesensor and the one or more components of the sensor electronics in thesterile package.

In an embodiment of the first aspect or any other embodiment thereof,the first time is during a manufacturing life phase of the analytesensor and the second time is during sensor use in vivo.

In an embodiment of the first aspect or any other embodiment thereof,monitoring the one or more environmental parameters includes monitoringa temperature of the analyte sensor while in a sterile package.

In an embodiment of the first aspect or any other embodiment thereof,monitoring the temperature includes measuring an impedance of theanalyte sensor by: applying a stimulus signal to the analyte sensor;measuring a signal response to the stimulus signal; calculating theimpedance based on the signal response; determining a value for thetemperature based on an established relationship between the impedanceand the temperature.

In an embodiment of the first aspect or any other embodiment thereof,monitoring the temperature includes measuring the temperature using atemperature sensor included in the sterile package, the temperaturesensor being operatively couplable to the sensor electronics.

In an embodiment of the first aspect or any other embodiment thereof,monitoring the one or more environmental parameters includes monitoringa humidity of the analyte sensor environment while in a sterile package.

In an embodiment of the first aspect or any other embodiment thereof,monitoring the humidity includes measuring an impedance of the analytesensor by: applying a stimulus signal to the analyte sensor; measuring asignal response to the stimulus signal; calculating the impedance basedon the signal response; determining a value for the humidity based on anestablished relationship between the impedance and the humidity.

In an embodiment of the first aspect or any other embodiment thereof,monitoring the humidity includes measuring the humidity using a humiditysensor included in the sterile package, the humidity sensor beingoperatively couplable to the sensor electronics.

In an embodiment of the first aspect or any other embodiment thereof,monitoring the one or more environmental parameters includes monitoringa sterilization dosage used to sterilize the analyte sensor.

In an embodiment of the first aspect or any other embodiment thereof,determining the change to the initial characteristic metric includesdetermining the change through use of a mathematical function.

In an embodiment of the first aspect or any other embodiment thereof,the manufacturing parameters are obtained from an identifier of theanalyte sensor.

In an embodiment of the first aspect or any other embodiment thereof,the identifier is affixed to the analyte sensor.

In an embodiment of the first aspect or any other embodiment thereof,the identifier is obtained by wirelessly interrogating the analytesensor.

In an embodiment of the first aspect or any other embodiment thereof,the identifier is associated with a manufacturing lot from which theanalyte sensor was obtained.

In an embodiment of the first aspect or any other embodiment thereof, auser is selected to receive the analyte sensor system based at least inpart on one or more analyte sensor characteristics.

In an embodiment of the first aspect or any other embodiment thereof theone or more sensor characteristics includes an updated characteristicmetric that is derived from the determined change to the initialcharacteristic metric.

In an embodiment of the first aspect or any other embodiment thereof,values for the monitored environmental parameters are stored forsubsequent use when automatically calibrating the analyte sensor system.

In an embodiment of the first aspect or any other embodiment thereof,monitoring the temperature of the analyte sensor while in the sterilepackage includes determining if the temperature exceeds or falls belowpre-established threshold values.

In an embodiment of the first aspect or any other embodiment thereofmonitoring the temperature of the analyte sensor while in the sterilepackage includes determining if the humidity exceeds or falls belowpre-established threshold values.

In an embodiment of the first aspect or any other embodiment thereof,the initial characteristic metric is reflective of an initial sensorsensitivity.

In an embodiment of the first aspect or any other embodiment thereof,the initial characteristic metric is reflective of an initial sensorsensitivity and baseline value.

In an embodiment of the first aspect or any other embodiment thereof,the initial characteristic metric is reflective of an initial sensorsensitivity profile.

In an embodiment of the first aspect or any other embodiment thereof, aninitial calibration factor is derived from the sensor characteristicmetric.

In an embodiment of the first aspect or any other embodiment thereof,the change to the initial sensor characteristic is indicative of sensorfailure.

In an embodiment of the first aspect or any other embodiment thereof,the one or more manufacturing parameters are measured prior to thesecond time.

In an embodiment of the first aspect or any other embodiment thereof,the one or more manufacturing parameters are measured prior to the firsttime.

In a second aspect, a method is provided for self-calibration of ananalyte sensor system that includes an analyte sensor operativelycoupled to sensor electronics, comprising: applying a bias voltage withthe sensor electronics to the analyte sensor to generate sensor data,the analyte sensor system having an initial characteristic metricdetermined at a first time when the analyte sensor is operativelyconnected to one or more components of the sensor electronics; using thesensor electronics at a second time subsequent to the first time todetermine a change to the initial characteristic metric of the analytesensor system based at least in part on one or more manufacturing and/orenvironmental parameters, wherein the second time is before or duringsensor use in vivo; and using the sensor electronics to automaticallycalibrate, without user intervention, the analyte sensor system based atleast in part on the determined change to the initial characteristicmetric.

In a third aspect, a method is provided for self-calibrating an analytesensor system that includes an analyte sensor operatively coupled tosensor electronics, comprising: applying a bias voltage with the sensorelectronics to the analyte sensor to generate sensor data, the analytesensor system having an initial calibration factor that is used toconvert sensor data to analyte concentration values; using the sensorelectronics to update the calibration factor of the analyte sensorsystem at a plurality of times during one or more life phases of theanalyte sensor based at least in part on one or more manufacturingand/or environmental parameters that are monitored during one or morelife phases; and using the sensor electronics to automaticallycalibrate, without user intervention, the analyte sensor system based atleast in part on the updated calibration factor.

In an embodiment of the third aspect or any other embodiment thereof,the one or more life phases include a plurality of life phases.

In an embodiment of the third aspect or any other embodiment thereof,the plurality of life phases includes manufacturing, shipping, storage,insertion and use phases.

In an embodiment of the third aspect or any other embodiment thereof,using the sensor electronics to update the calibration factor of theanalyte sensor system includes determining a complex adaptivecalibration value that is based at least in part on manufacturingconditions and environmental conditions experienced by the analytesensor during a plurality of the life phases of the analyte sensor.

In an embodiment of the third aspect or any other embodiment thereof,the manufacturing parameters include process and/or design parameters.

In an embodiment of the third aspect or any other embodiment thereof,the manufacturing parameters include process parameters, the processparameters including temperature, humidity, curing, time and dip time.

In an embodiment of the third aspect or any other embodiment thereof,the manufacturing parameters include design parameters, the designparameters including analyte sensor membrane thickness and raw materialcharacteristics.

In an embodiment of the third aspect or any other embodiment thereof,the sensor electronics is used to receive remotely stored sensorperformance data to update the calibration factor.

In an embodiment of the third aspect or any other embodiment thereof,the remotely stored sensor performance data that is received concernsanalyte sensors that have experienced or been exposed to manufacturingand/or environmental parameters that are most similar to one or more ofthe monitored manufacturing and/or environmental parameters.

In a fourth aspect, a method is provided in which the sensor experiencesa plurality of life phases including manufacture, shipping, storage andinsertion and use in a user as part of a sensor session, comprising:disposing measurement electronics in operable connection to the sensor;during the manufacture life phase in a factory, the manufacture lifephase manufacturing a sensor using a plurality of manufacturingparameters, determining a first calibration factor; during the shippingor storage phases, determining a second calibration factor; and uponinsertion in a user, using a combination calibration factor in a usermonitoring device to calibrate signals from the sensor, wherein thecombination calibration factor is based on both the first calibrationfactor and the second calibration factor.

In an embodiment of the fourth aspect or any other embodiment thereof,the first calibration factor is stored in the sensor electronics or inmeasurement electronics associated with the sensor assembly.

In an embodiment of the fourth aspect or any other embodiment thereof,the measurement electronics form a part of the sensor electronics.

In an embodiment of the fourth aspect or any other embodiment thereof,the measurement electronics are separate from the sensor electronics.

In an embodiment of the fourth aspect or any other embodiment thereof,the measurement electronics is disposed in the same package as thesensor electronics.

In an embodiment of the fourth aspect or any other embodiment thereof,the measurement electronics is disposed in a different package than thesensor electronics.

In an embodiment of the fourth aspect or any other embodiment thereof,the sensor assembly and the measurement electronics are disposed in apackage for shipping.

In an embodiment of the fourth aspect or any other embodiment thereof,the user monitoring device is a dedicated receiver or a smart phone.

In an embodiment of the fourth aspect or any other embodiment thereof,the transmitting is from the sensor electronics or the measurementelectronics to the dedicated receiver or the smart phone.

In an embodiment of the fourth aspect or any other embodiment thereof,the second calibration factor is stored within the measurementelectronics or the sensor electronics.

In an embodiment of the fourth aspect or any other embodiment thereof,the combination calibration factor is transmitted to a cloud server.

In an embodiment of the fourth aspect or any other embodiment thereof,the combination calibration factor, or the second calibration factor, orboth, are transmitted to the factory, to cause a change in one of theplurality of manufacturing parameters.

In an embodiment of the fourth aspect or any other embodiment thereof,measuring a second calibration factor is performed by the measurementelectronics.

In an embodiment of the fourth aspect or any other embodiment thereof,the first calibration factor is a system level calibration factorpertaining to the calibration of all of the components in the sensorassembly.

In an embodiment of the fourth aspect or any other embodiment thereof,the transmitting further comprises transmitting a sensor tracking numberor serial number to a cloud server or to the factory along with thecombination calibration factor, whereby a lot associated with the sensorcan be identified.

In an embodiment of the fourth aspect or any other embodiment thereof,the measurement electronics are configured to detect faults in thesensor electronics or sensor.

In an embodiment of the fourth aspect or any other embodiment thereof,the transmitting further comprises transmitting data about detectedfaults in the sensor electronics or sensor.

In an embodiment of the fourth aspect or any other embodiment thereof, acalibration factor stored in the user monitoring device is modified tocompensate for the detected fault.

In an embodiment of the fourth aspect or any other embodiment thereof,the measurement electronics are configured to detect electrical signalsfrom the sensor wire, the sensor electronics, the housing, or acombination.

In an embodiment of the fourth aspect or any other embodiment thereof,the combination calibration factor is configured to correct forindividual process and shipping/storage variations of an in vivo sensor.

In an embodiment of the fourth aspect or any other embodiment thereof,the first calibration factor or the second calibration factor, or both,are indicative of a measured impedance.

In an embodiment of the fourth aspect or any other embodiment thereof,the impedance measurement is performed by measuring a step response at asingle frequency or at multiple frequencies.

In an embodiment of the fourth aspect or any other embodiment thereof, athird calibration factor is measured prior to shipping, and wherein thethird calibration factor is indicative of impedance.

In an embodiment of the fourth aspect or any other embodiment thereof,the first calibration factor or the second calibration factor, or both,are indicative of a measured temperature.

In an embodiment of the fourth aspect or any other embodiment thereof,the first calibration factor or the second calibration factor, or both,are indicative of a measured humidity.

In an embodiment of the fourth aspect or any other embodiment thereof,the combination calibration factor is used to calculate a modifiedcalibration value, detect physical damage to the sensor, or detectexposure of the sensor assembly to temperature and/or humidity.

In an embodiment of the fourth aspect or any other embodiment thereof,the combination calibration factor is a complex adaptive value thatcombines calibration values collected during sensor manufacturer andconditions experienced during the time from sensor manufacturer tosensor insertion.

In an embodiment of the fourth aspect or any other embodiment thereof, auser is selected to receive the sensor based on the first calibrationfactor, whereby population data or individual user data determines thatthe sensor with the first calibration factor is optimized for the user.

In an embodiment of the fourth aspect or any other embodiment thereof,the user is known to have a high average glucose level, and wherein thefirst calibration factor is a relatively low sensitivity.

In an embodiment of the fourth aspect or any other embodiment thereof,the manufacturing life phase includes a packaging phase in which thepreconnected sensor assembly is packaged in a sterile package, the firstcalibration factor being determined after the preconnected sensorassembly is packaged in the sterile package.

In a fifth aspect, an improved method is provided of calibrating asensor associated with a preconnected sensor assembly in which thesensor experiences a plurality of life phases including manufacture,shipping, storage and insertion and use in a user as part of a sensorsession, comprising: disposing measurement electronics in operableconnection to the sensor electronics; during the manufacture life phasein a factory, the manufacture life phase manufacturing a sensor using aplurality of manufacturing parameters, determining a first calibrationfactor; during the shipping or storage phases, measuring a secondcalibration factor; and upon insertion in a user, calculating acombination calibration factor and storing the same within the sensorelectronics, wherein the combination calibration factor is based on boththe first calibration factor and the second calibration factor, whereinthe combination calibration factor is configured to provide a conversionbetween a detected signal from the sensor wire and an analyteconcentration in the user.

In an embodiment of the fifth aspect or any other embodiment thereof, anindication is displayed of the analyte concentration.

In an embodiment of the fifth aspect or any other embodiment thereof,the displaying occurs on a user monitoring device in signalcommunication with the sensor electronics.

In an embodiment of the fifth aspect or any other embodiment thereof,the user monitoring device is a dedicated receiver or a smart phone.

In a sixth aspect, an improved method is provided of manufacturing asensor assembly including a sensor wire, a housing, and sensorelectronics, comprising: pre-connecting at least a sensor wire to atleast a portion of the sensor electronics sufficient to monitormanufacturing parameters; monitoring the manufacturing parameters whilecompleting manufacturing of the sensor assembly; modifying one or moreof the manufacturing parameters during subsequent manufacturingprocesses used to manufacture additional sensor assemblies, themodifying being based at least in part on the monitored manufacturingparameters.

In an embodiment of the sixth aspect or any other embodiment thereof,the sensor electronics is preconnected to the sensor wire but where thebattery and radio remain disconnected.

In an embodiment of the sixth aspect or any other embodiment thereof,the battery is pre-connected to the sensor wire and the portion of thesensor electronics sufficient to monitor manufacturing parameters.

In an embodiment of the sixth aspect or any other embodiment thereof,the radio is preconnected to the battery and the sensor wire and theportion of the sensor electronics sufficient to monitor manufacturingparameters.

In an embodiment of the sixth aspect or any other embodiment thereof,the combined error of the preconnected sensor wire and the portion ofthe sensor electronics sufficient to monitor manufacturing parameters inis less than a propagated or summed error of the sensor wire and theportion of the sensor electronics sufficient to monitor manufacturingparameters considered individually.

In seventh aspect, an improved preconnected sensor assembly is providedthat includes a sensor wire, a housing, and sensor electronics, wherethe sensor is preconnected to a housing and/or to sensor electronics,and wherein the sensor wire is at least preconnected to an interposerwhich is configured for allowing measurements of sensor physicalproperties without requiring a direct connection to the sensor wire.

In an embodiment of the seventh or any other embodiment thereof, thebattery is preconnected to the sensor wire and the housing and/or sensorelectronics.

In an embodiment of the seventh aspect or any other embodiment thereof,the radio is preconnected to the battery and the sensor wire and thehousing and/or sensor electronics.

In an eighth aspect, a method is provided for self-calibration of ananalyte sensor system that includes an analyte sensor operativelycouplable to sensor electronics, comprising: operatively coupling at afirst time the analyte sensor to one or more components of the sensorelectronics to define a packagable analyte sensor arrangement, thepackagable sensor arrangement having an initial sensitivity metricdetermined subsequent to the first time; applying an analyteinterrogation signal with the one or more components of the sensorelectronics to the analyte sensor at a second time subsequent to thefirst time; measuring a signal response to the stimulus signal; based atleast in part on the measured signal response, determining a secondsensitivity metric; automatically calibrating, without userintervention, the packagable sensor arrangement based at least in parton the initial sensitivity metric and the second sensitivity metric.

In an embodiment of the eighth aspect or any other embodiment thereof,the analyte sensor is continuously operatively coupled to the one ormore components of the sensor electronics between the first and secondtimes without interruption.

In an embodiment of the eighth aspect or any other embodiment thereof,applying an analyte interrogation signal includes applying a stimulussignal to the analyte sensor and measuring the signal response includesmeasuring an impedance of the packagable analyte sensor arrangement.

In an embodiment of the eighth aspect or any other embodiment thereof,automatically calibrating the packagable sensor arrangement is based onan established relationship between the impedance and analyte sensorsensitivity, wherein automatically calibrating the packagable sensorarrangement includes automatically calibrating the packagable sensorarrangement in vivo.

In a ninth aspect, a method is provided for performing an action with ananalyte sensor system that includes an analyte sensor operativelycoupled to sensor electronics, comprising: applying a bias voltage withthe sensor electronics to the analyte sensor to generate sensor data,the analyte sensor system having an initial characteristic metricdetermined at a first time when the analyte sensor is operativelyconnected to one or more components of the sensor electronics; using thesensor electronics at a second time subsequent to the first time todetermine a change to the initial characteristic metric of the analytesensor system based at least in part on one or more manufacturing and/orenvironmental parameters, wherein the second time is before or duringsensor use in vivo; and based at least in part on the determined changeto the initial characteristic metric, performing an action selected fromthe group comprising: generating a message, initiating a re-calibrationprocess, using a default calibration value and using a temperatureand/or humidity compensated calibration value.

In an embodiment of the ninth aspect or any other embodiment thereof,generating the message includes generating an error message.

In an embodiment of the ninth aspect or any other embodiment thereof,generating the message includes generating a message requesting a manualrecalibration.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages will be appreciated, as theybecome better understood by reference to the following detaileddescription when considered in connection with the accompanyingdrawings, wherein:

FIG. 1 is a schematic view of an analyte sensor system attached to ahost and communicating with a plurality of example devices, according tosome embodiments.

FIG. 2 is a block diagram that illustrates electronics associated withthe sensor system of FIG. 1 , according to some embodiments.

FIG. 3 illustrates a perspective view of a wearable device having ananalyte sensor, according to some embodiments.

FIG. 4 illustrates a schematic of a preconnected analyte sensor,according to some embodiments.

FIG. 5 illustrates a block diagram of a system having a manufacturingsystem and a wearable device for an analyte sensor, according to someembodiments.

FIG. 6 illustrates a schematic diagram of sensor sensitivity as afunction of time during a sensor session, in accordance with oneembodiment;

FIG. 7 illustrates schematic diagrams of conversion functions atdifferent time periods of a sensor session, in accordance with theembodiment of FIG. 6 .

FIGS. 8A-8B show examples of various phases in an analyte sensor systemlifecycle.

FIG. 9 shows a schematic block diagram of one particular example of apreconnected analyte sensor system

FIG. 10 is a Monte Carlo simulation of 5000 samples using a randomlyselected number within the statistical distribution of the inputvariables that compares a non-preconnected system and a preconnectedsystem.

FIG. 11 shows an example of an automatic calibration process that may beperformed by the sensor electronics in the analyte monitoring systemwithout user intervention.

FIG. 12 includes timelines showing the monitored temperature (a),humidity (b) and sensitivity (c), respectively, over various phases overthe lifetime of an analyte sensor.

FIG. 13 shows a sensor output signal obtained from an analyte sensorduring various steps during the manufacturing process.

FIG. 14 shows the NMR spectrum of PVP in DMSO-d6.

FIG. 15 shows the HNMR spectrum of Carbosil in DMSO.

FIG. 16 shows the HNMR spectrum of an RL film (Carbosil/PVP blend withremoval of solvent).

FIG. 17 shows a composition of an RL solution that was prepared withdifferent Carbosil/PVP ratios.

FIG. 18 shows an HNMR calibration curve.

FIG. 19 is a graph showing initial sensor drift when ethylene oxide(ETO) sterilization is employed.

FIG. 20 shows an example of various life phases that an analyte sensormay undergo.

DETAILED DESCRIPTION Definitions

In order to facilitate an understanding of the embodiments describedherein, a number of terms are defined below.

The term “analyte,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is are not to be limited to a special or customized meaning),and refers without limitation to a substance or chemical constituent ina biological fluid (for example, blood, interstitial fluid, cerebralspinal fluid, lymph fluid or urine) that can be analyzed. Analytes mayinclude naturally occurring substances, artificial substances,metabolites, and/or reaction products. In some embodiments, the analytefor measurement by the sensor heads, devices, and methods disclosedherein is glucose. However, other analytes are contemplated as well,including but not limited to acarboxyprothrombin; acylcarnitine; adeninephosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, analyte-6-phosphate dehydrogenase,hemoglobinopathies, A,S,C,E, D-Punjab, beta-thalassemia, hepatitis Bvirus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD,RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol);desbutylhalofantrine; dihydropteridine reductase; diptheria/tetanusantitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D;fatty acids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I; 17alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins and hormones naturally occurring in blood or interstitialfluids may also constitute analytes in certain embodiments. The analytemay be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte may be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbiturates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body may also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The terms “continuous analyte sensor,” and “continuous glucose sensor,”as used herein, are broad terms, and are to be given their ordinary andcustomary meaning to a person of ordinary skill in the art (and are notto be limited to a special or customized meaning), and refer withoutlimitation to a device that continuously or continually measures aconcentration of an analyte/glucose and/or calibrates the device (e.g.,by continuously or continually adjusting or determining the sensor'ssensitivity and background), for example, at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes, or longer.

The term “biological sample,” as used herein, is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to sample derived from the bodyor tissue of a host, such as, for example, blood, interstitial fluid,spinal fluid, saliva, urine, tears, sweat, or other like fluids.

The term “host,” as used herein, is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andrefers without limitation to animals, including humans.

The term “membrane system,” as used herein, is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a permeable or semi-permeablemembrane that can be comprised of two or more domains and is typicallyconstructed of materials of a few microns thickness or more, which maybe permeable to oxygen and are optionally permeable to glucose. In oneexample, the membrane system comprises an immobilized glucose oxidaseenzyme, which enables an electrochemical reaction to occur to measure aconcentration of glucose.

The term “domain,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to regions of a membrane that can be layers,uniform or non-uniform gradients (for example, anisotropic), functionalaspects of a material, or provided as portions of the membrane.

The term “sensing region,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the region of a monitoringdevice responsible for the detection of a particular analyte. In oneembodiment, the sensing region generally comprises a non-conductivebody, at least one electrode, a reference electrode and a optionally acounter electrode passing through and secured within the body forming anelectroactive surface at one location on the body and an electronicconnection at another location on the body, and a membrane systemaffixed to the body and covering the electroactive surface.

The term “electroactive surface,” as used herein, is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and refers without limitation to the surface of anelectrode where an electrochemical reaction takes place. In oneembodiment, a working electrode measures hydrogen peroxide (H₂O₂)creating a measurable electronic current.

The term “baseline,” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to the component of an analyte sensor signalthat is not related to the analyte concentration. In one example of aglucose sensor, the baseline is composed substantially of signalcontribution due to factors other than glucose (for example, interferingspecies, non-reaction-related hydrogen peroxide, or other electroactivespecies with an oxidation potential that overlaps with hydrogenperoxide). In some embodiments wherein a calibration is defined bysolving for the equation y=mx+b, the value of b represents the baselineof the signal. In certain embodiments, the value of b (i.e., thebaseline) can be zero or about zero. This can be the result of abaseline-subtracting electrode or low bias potential settings, forexample. As a result, for these embodiments, calibration can be definedby solving for the equation y=mx.

The term “inactive enzyme,” as used herein, is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to an enzyme (e.g., glucoseoxidase, GOx) that has been rendered inactive (e.g., by denaturing ofthe enzyme) and has substantially no enzymatic activity. Enzymes can beinactivated using a variety of techniques known in the art, such as butnot limited to heating, freeze-thaw, denaturing in organic solvent,acids or bases, cross-linking, genetically changing enzymaticallycritical amino acids, and the like. In some embodiments, a solutioncontaining active enzyme can be applied to the sensor, and the appliedenzyme subsequently inactivated by heating or treatment with aninactivating solvent.

The term “non-enzymatic,” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a lack of enzyme activity. Insome embodiments, a “non-enzymatic” membrane portion contains no enzyme;while in other embodiments, the “non-enzymatic” membrane portioncontains inactive enzyme. In some embodiments, an enzyme solutioncontaining inactive enzyme or no enzyme is applied.

The term “substantially,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to being largely but notnecessarily wholly that which is specified.

The term “about,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andwhen associated with any numerical values or ranges, refers withoutlimitation to the understanding that the amount or condition the termsmodify can vary some beyond the stated amount so long as the function ofthe disclosure is realized.

The term “ROM,” as used herein, is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andrefers without limitation to read-only memory, which is a type of datastorage device manufactured with fixed contents. ROM is broad enough toinclude EEPROM, for example, which is electrically erasable programmableread-only memory (ROM).

The term “RAM,” as used herein, is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andrefers without limitation to a data storage device for which the orderof access to different locations does not affect the speed of access.RAM is broad enough to include SRAM, for example, which is static randomaccess memory that retains data bits in its memory as long as power isbeing supplied.

The term “A/D Converter,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to hardware and/or software thatconverts analog electrical signals into corresponding digital signals.

The terms “raw data stream” and “data stream,” as used herein, are broadterms, and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and refer without limitation to ananalog or digital signal directly related to the analyte concentrationmeasured by the analyte sensor. In one example, the raw data stream isdigital data in counts converted by an A/D converter from an analogsignal (for example, voltage or amps) representative of an analyteconcentration. The terms broadly encompass a plurality of time spaceddata points from a substantially continuous analyte sensor, whichcomprises individual measurements taken at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes or longer.

The term “counts,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to a unit of measurement of a digital signal.In one example, a raw data stream measured in counts is directly relatedto a voltage (for example, converted by an A/D converter), which isdirectly related to current from a working electrode.

The term “sensor electronics,” as used herein, is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the components (for example,hardware and/or software) of a device configured to process data. In thecase of an analyte sensor, the data includes biological informationobtained by a sensor regarding the concentration of the analyte in abiological fluid. U.S. Pat. Nos. 4,757,022, 5,497,772 and 4,787,398describe suitable electronic circuits that can be utilized with devicesof certain embodiments.

The term “potentiostat,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to an electrical system thatapplies a potential between the working and reference electrodes of atwo- or three-electrode cell at a preset value and measures the currentflow through the working electrode. The potentiostat forces whatevercurrent is necessary to flow between the working and counter electrodesto keep the desired potential, as long as the needed cell voltage andcurrent do not exceed the compliance limits of the potentiostat.

The term “operably connected,” as used herein, is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to one or more components beinglinked to another component(s) in a manner that allows transmission ofsignals between the components. For example, one or more electrodes canbe used to detect the amount of glucose in a sample and convert thatinformation into a signal; the signal can then be transmitted to anelectronic circuit. In this case, the electrode is “operably linked” tothe electronic circuit. These terms are broad enough to include wiredand wireless connectivity.

The term “filtering,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to modification of a set of datato make it smoother and more continuous and remove or diminish outlyingpoints, for example, by performing a moving average of the raw datastream.

The term “algorithm,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the computational processes(for example, programs) involved in transforming information from onestate to another, for example using computer processing.

The term “calibration,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the process of determiningthe graduation of a sensor giving quantitative measurements (e.g.,analyte concentration). As an example, calibration may be updated orrecalibrated over time to account for changes associated with thesensor, such as changes in sensor sensitivity and sensor background. Inaddition, calibration of the sensor can involve, automatic,self-calibration, e.g., without using reference analyte values afterpoint of use.

The terms “sensor data,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to data received from acontinuous analyte sensor, including one or more time-spaced sensor datapoints.

The terms “reference analyte values” and “reference data,” as usedherein, are broad terms, and are to be given their ordinary andcustomary meaning to a person of ordinary skill in the art (and are notto be limited to a special or customized meaning), and refer withoutlimitation to reference data from a reference analyte monitor, such as ablood glucose meter, or the like, including one or more reference datapoints. In some embodiments, the reference glucose values are obtainedfrom a self-monitored blood glucose (SMBG) test (for example, from afinger or forearm blood test) or a YSI (Yellow Springs Instruments)test, for example.

The terms “interferents” and “interfering species,” as used herein, arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and refer without limitation to effectsand/or species that interfere with the measurement of an analyte ofinterest in a sensor to produce a signal that does not accuratelyrepresent the analyte measurement. In one example of an electrochemicalsensor, interfering species are compounds with an oxidation potentialthat overlaps with the analyte to be measured, producing a falsepositive signal.

The term “sensor session,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the period of time the sensoris applied to (e.g. implanted in) the host or is being used to obtainsensor values. For example, in some embodiments, a sensor sessionextends from the time of sensor implantation (e.g. including insertionof the sensor into subcutaneous tissue and placing the sensor into fluidcommunication with a host's circulatory system) to the time when thesensor is removed.

The terms “sensitivity” or “sensor sensitivity,” as used herein, arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and refer without limitation to anamount of signal produced by a certain concentration of a measuredanalyte, or a measured species (e.g., H₂O₂) associated with the measuredanalyte (e.g., glucose). For example, in one embodiment, a sensor has asensitivity of from about 1 to about 300 picoAmps of current for every 1mg/dL of glucose analyte.

The term “sensitivity profile” or “sensitivity curve,” as used herein,are broad terms, and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and is not to belimited to a special or customized meaning), and refer withoutlimitation to a representation of a change in sensitivity over time.

The term “process set point,” as used herein, is broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a desired or target value fora variable or process value of a system.

The term “process variability” as used herein, is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a measure of the deviationfrom a set point and is usually expressed as a standard deviation.

The term “Monte Carlo Simulation,” as used herein, is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and refers without limitation to defining a domainof possible inputs, generating inputs randomly from a probabilitydistribution over the domain, performing a deterministic computation onthe inputs and aggregating the results. Monte Carlo simulations samplefrom a probability distribution for each variable to produce hundreds orthousands of possible outcomes. The results are analyzed to getprobabilities of different outcomes occurring.

The term “accuracy,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to the closeness of a measured value to astandard or known value.

The term “precision,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the degree to which repeatedmeasurements under unchanged conditions show the same results.

Overview

Commercially available transcutaneous analyte measurement systemsconsist of discrete modules that are physically interconnectedimmediately prior or just following final sensor placement. Generally,the analyte sensor module is characterized by a variety of measurementfactors (e.g. analyte sensitivity, baseline, impedance, capacitance,temperature, time, humidity, interferent sensitivity, etc.) Thesecharacteristics have historically been quantified once at the completionof the analyte sensor manufacturing process using manufacturing testequipment. These measurements are taken on the sensor subsystem usingtest configurations such as placing the analyte sensor in one or moresolutions of known analyte concentration.

The measurements derived from the manufacturing process on the analytesensor are sometimes used to create one or more metrics of the sensorperformance. These metrics can be transferred using various methods(e.g. calibration code, wireless transfer, lot matching) to an analytealgorithm processing unit. In other embodiments the measurements areused to determine if an individual sensor or lot of sensors meetsacceptable quality criteria. Analyte sensor metrics, in-vivocalibrations, environmental condition sensors, and a priori informationare typical inputs to an analyte algorithm processing unit.

Conventional in vivo continuous analyte sensing technology has typicallyrelied on reference measurements performed during a sensor session forcalibration of the continuous analyte sensor. The reference measurementsare matched with substantially time corresponding sensor data to creatematched data pairs. Regression is then performed on the matched datapairs (e.g., by using least squares regression) to generate a conversionfunction that defines a relationship between a sensor signal and anestimated glucose concentration.

In critical care settings, calibration of continuous analyte sensors isoften performed by using, as reference, a calibration solution with aknown concentration of the analyte. This calibration procedure can becumbersome, as a calibration bag, separate from (and an addition to) anIV (intravenous) bag, is typically used. In the ambulatory setting,calibration of continuous analyte sensors has traditionally beenperformed by capillary blood glucose measurements (e.g., a finger stickglucose test), through which reference data is obtained and input intothe continuous analyte sensor system. This calibration proceduretypically involves frequent finger stick measurements, which can beinconvenient and painful.

Heretofore, systems and methods for in vitro calibration of a continuousanalyte sensor by the manufacturer (e.g., factory calibration), withoutreliance on periodic recalibration, have for the most part beeninadequate with respect to high levels of sensor accuracy. Part of thiscan be attributed to changes in sensor properties (e.g., sensorsensitivity) that can occur during sensor use. Thus, calibration ofcontinuous analyte sensors has typically involved periodic inputs ofreference data, whether they are associated with a calibration solutionor with a finger stick measurement. As noted, such can be veryburdensome to the patient no matter the setting.

Described herein are continuous analyte sensors that are factorycalibrated or are capable of continuous, automatic self-calibrationduring a sensor session and capable of achieving high levels ofaccuracy, without (or with reduced) reliance on reference data from areference analyte monitor (e.g., from a blood glucose meter). Factorycalibration refers generally to an initial calibration that is typicallyperformed before the sensor leaves the factory and which is not changedover time. Automatic self-calibration, on the other hand, refers to aprocess in which the calibration is updated without user intervention atone or more intervals of time subsequent to factory calibration, wherethe updating is based on information obtained during manufacturingand/or during later life phases of the analyte sensor. The updating ofthe calibration is generally accomplished by sending a signal from e.g.,the cloud, to the sensor or the sensor electronics.

FIG. 20 shows an example of various life phases that an analyte sensormay undergo, which illustratively include a sensor manufacturing phase,a sensor packaging phase, a sensor storage phase, a pre in vivo phase,and a sensor session phase. As will be discussed in more detail below,in some cases the sensor may undergo additional, or fewer, life phasesas well. At various times t during these phases a complex adaptivecalibration factor C(t, p_(i)) may be generated that is a function ofthe time t since the sensor was manufactured and various parametersp_(i), where i≥1. The parameters p_(i) represent, for instance,environmental conditions experienced by the analyte sensor (and anypreconnected electronics, if present) from sensor manufacture to sensoruse during the sensor session phase, possibly combined with additionalinformation such as patient-specific data. The complex adaptivecalibration factor may reflect changes to the analyte sensor (and anypreconnected electronics, if present) that have arisen since one or moreinitial calibration factors C_(m) were obtained during sensormanufacture. For instance, in FIG. 20 an initial calibration factorC_(M) is obtained by a “cal check” procedure in the factory during whichthe sensor undergoes in vitro calibration. At subsequent times, such ast1 (during the shipping phase) and t2 (during the sensor session phase),for example, complex adaptive calibration factors C_(t1) and C_(t2) maybe respectively obtained using C_(M1) and the measured values of theparameters. Additional complex adaptive calibration factors may beobtained during the shipping phase (e.g., C_(S1), C_(S2) . . . C_(Sn)),the storage phase (C_(ST1) and C_(ST2)), the pre in vivo phase (e.g.,C_(P)) and the sensor session phase (e.g., C_(SS1), C_(SS2) . . .C_(SSn)). In this way the experience of the analyte sensor during itslifetime is encoded in a form that allows it to be used by a suitablecalibration algorithm to determine, for instance, the sensitivity of thesensor and/or its baseline value.

In some embodiments, the continuous analyte sensor is an invasive,minimally invasive, or non-invasive device. The continuous analytesensor can be a subcutaneous, transdermal, or intravascular device. Incertain embodiments, one or more of these devices may form a continuousanalyte sensor system. For instance, the continuous analyte sensorsystem may be comprised of a combination of a subcutaneous device and atransdermal device, a combination of a subcutaneous device and anintravascular device, a combination of a transdermal device and anintravascular device, or a combination of a subcutaneous device, atransdermal device, and an intravascular device. In some embodiments,the continuous analyte sensor can analyze a plurality of intermittentbiological samples (e.g., blood samples). The continuous analyte sensorcan use any glucose-measurement method, including methods involvingenzymatic, chemical, physical, electrochemical, spectrophotometric,polarimetric, calorimetric, iontophoretic, and radiometric mechanisms,and the like.

In certain embodiments, the continuous analyte sensor includes one ormore working electrodes and one or more reference electrode, whichoperate together to measure a signal associated with a concentration ofthe analyte in the host. The output signal from the working electrode istypically a raw data stream that is calibrated, processed, and used togenerate an estimated analyte (e.g., glucose) concentration. In certainembodiments, the continuous analyte sensor may measure an additionalsignal associated with the baseline and/or sensitivity of the sensor,thereby enabling monitoring of baseline and/or additional monitoring ofsensitivity changes or drift that may occur in a continuous analytesensor over time.

In some embodiments, the sensor extends through a housing, whichmaintains the sensor on the skin and provides for electrical connectionof the sensor to sensor electronics. In one embodiment, the sensor isformed from a wire. For example, the sensor can include an elongatedconductive body, such as a bare elongated conductive core (e.g., a metalwire) or an elongated conductive core coated with one, two, three, four,five, or more layers of material, each of which may or may not beconductive. The elongated sensor may be long and thin, yet flexible andstrong. For example, in some embodiments the smallest dimension of theelongated conductive body is less than about 0.1 inches, 0.075 inches,0.05 inches, 0.025 inches, 0.01 inches, 0.004 inches, or 0.002 inches.Other embodiments of the elongated conductive body are disclosed in U.S.Patent Application Publication No. 2011-0027127-A1, which isincorporated herein by reference in its entirety. Preferably, a membranesystem is deposited over at least a portion of electroactive surfaces ofthe sensor 102 (including a working electrode and optionally a referenceelectrode) and provides protection of the exposed electrode surface fromthe biological environment, diffusion resistance (limitation) of theanalyte if needed, a catalyst for enabling an enzymatic reaction,limitation or blocking of interferents, and/or hydrophilicity at theelectrochemically reactive surfaces of the sensor interface. Disclosuresregarding the different membrane systems that may be used with theembodiments described herein are described in U.S. Patent PublicationNo. US-2009-0247856-A1, which is incorporated herein by reference in itsentirety.

In the prior art, calibrating sensor data from continuous analytesensors generally involved defining a relationship betweensensor-generated measurements (e.g., in units of nA or digital countsafter A/D conversion) and one or more reference measurement (e.g., inunits of mg/dL or mmol/L). In certain embodiments, one or more referencemeasurements obtained shortly after the analyte sensor is manufactured,and before sensor use, are used for calibration. The referencemeasurement may have been obtained in many forms. For example, incertain cases, the reference measurement may be determined from in vivoanalyte concentration measurements.

With factory calibration or automatic self-calibration, the need forrecalibration, by using reference data during a sensor session, may beeliminated, or else lessened, such that recalibration may be called foronly in certain limited circumstances, such as when sensor failure isdetected. Additionally or alternatively, in some embodiments, thecontinuous analyte sensor may be configured to request and accept one ormore reference measurements (e.g., from a finger stick glucosemeasurement or a calibration solution) at the start of the sensorsession. In some embodiments, use of a reference measurement at thestart of the sensor session in conjunction with a predetermined sensorsensitivity profile can eliminate or substantially reduce the need forfurther reference measurements.

Turning to a basic description of glucose sensor functionality, withcertain implantable enzyme-based electrochemical glucose sensors, thesensing mechanism depends on certain phenomena that have a generallylinear relationship with glucose concentration, for example: (1)diffusion of an analyte through a membrane system situated between animplantation site (e.g., subcutaneous space) and an electroactivesurface, (2) rate of an enzyme-catalyzed reaction of the analyte toproduce a measured species within the membrane system (e.g., the rate ofa glucose oxidase-catalyzed reaction of glucose with O₂ which producesgluconic acid and H₂O₂), and (3) diffusion of the measured species(e.g., H₂O₂) to the electroactive surface. Because of this generallylinear relationship, calibration of the sensor is obtained by solvingthe equation:y=mx+bwherein y represents the sensor signal (counts), x represents theestimated glucose concentration (mg/dL), m represents the sensorsensitivity to analyte concentration (counts/mg/dL), and b representsthe baseline signal (counts). As described elsewhere herein, in certainembodiments, the value b (i.e., the baseline) can be zero or about zero.As a result, for these embodiments, calibration can be defined bysolving for the equation y=mx.

In some embodiments, the continuous analyte sensor system is configuredto estimate changes or drift in sensitivity of the sensor for an entiresensor session as a function of time (e.g., elapsed time since start ofthe sensor session). As described elsewhere herein, this sensitivityfunction plotted against time may resemble a curve. Additionally oralternatively, the system can also be configured to determine sensorsensitivity changes or drift as a function of time and one or more otherparameters that can also affect sensor sensitivity or provide additionalinformation about sensor sensitivity. These parameters can affect sensorsensitivity or provide additional information about sensor sensitivityprior to the sensor session, such as parameters associated with thesensor fabrication (e.g., materials used to fabricate sensor membrane,the thickness of the sensor membrane, the temperature at which thesensor membrane was cured, the length of time the sensor was dipped in aparticular coating solution, etc.). In certain embodiments, some of theparameters involve information, obtained prior to the sensor session,which can be accounted for in a calibration code that is associated witha particular sensor lot. Other parameters can be associated withconditions surrounding the sensor after its manufacture, but before thesensor session, such as, for example, the level of exposure of thesensor to certain levels of humidity or temperature while the sensor isin a package in transit from the manufacturing facility to the patient.Yet other parameters (e.g., sensor membrane permeability, temperature atthe sample site, pH at the sample site, oxygen level at the sample site,etc.) can affect sensor sensitivity or provide additional informationabout sensor sensitivity during the sensor session.

Determination of sensor sensitivity at different times of a sensorsession based on the predetermined sensor sensitivity profile can beperformed prior to the sensor session or at the start of the sensorsession. Additionally, in certain embodiments, determination of sensorsensitivity, based on the sensor sensitivity profile, can becontinuously adjusted to account for parameters that affect sensorsensitivity or provide additional information about sensor sensitivityduring the sensor session. These determinations of sensor sensitivitychange or drift can be used to provide self-calibration, updatecalibration, supplement calibration based on measurements of knownvalues (e.g., from a reference analyte monitor), and/or validate orreject reference analyte measurements from a reference analyte monitor.In some embodiments, validation or rejection of reference analytemeasurements can be based on whether the reference analyte measurementsare within a range of values associated with the predetermined sensorsensitivity profile.

Some of the continuous analyte sensors described herein may beconfigured to measure a signal associated with a non-analyte constantsignal in the host. Preferably, the non-analyte constant signal ismeasured beneath the membrane system on the sensor. In one example of acontinuous glucose sensor, a non-glucose constant signal that can bemeasured is oxygen. In some embodiments, a change in oxygen transport,which can be indicative of a change or drift in the sensitivity of theglucose signal, can be measured by switching the bias potential of theworking electrode, an auxiliary oxygen-measuring electrode, an oxygensensor, or the like.

Additionally, some of the continuous analyte sensors described hereinmay be configured to measure changes in the amount of background noisein the signal. Detection of changes which exceed a certain threshold canprovide the basis for triggering calibration, updating calibration,and/or validating or rejecting inaccurate reference analyte values froma reference analyte monitor. In one example of a continuous glucosesensor, the background noise is composed substantially of signalcontribution from factors other than glucose (for example, interferingspecies, non-reaction-related hydrogen peroxide, or other electroactivespecies with an oxidation potential that overlaps with hydrogenperoxide). Namely, the continuous glucose sensor is configured tomeasure a signal associated with the baseline (which includessubstantially all non-glucose related current generated), as measured bythe sensor in the host. In some embodiments, an auxiliary electrodelocated beneath a non-enzymatic portion of the membrane system is usedto measure the baseline signal. The baseline signal can be subtractedfrom the glucose+baseline signal to obtain a signal associated entirelyor substantially entirely with glucose concentration. Subtraction may beaccomplished electronically in the sensor using a differentialamplifier, digitally in the receiver, and/or otherwise in the hardwareor software of the sensor or receiver as described in more detailelsewhere herein.

Together, by determining sensor sensitivity based on a sensitivityprofile and by measuring a baseline signal, the continuous analytesensor can be continuously self-calibrated during a sensor sessionwithout (or with reduced) reliance on reference measurements from areference analyte monitor or calibration solution.

Sensor System

FIG. 1 depicts an example system 100, in accordance with some exampleimplementations. The system 100 includes an analyte sensor system 101including sensor electronics 112 and an analyte sensor 138. The system100 may include other devices and/or sensors, such as medicamentdelivery pump 102 and glucose meter 104. The analyte sensor 138 may bephysically connected to sensor electronics 112 and may be integral with(e.g., non-releasably attached to) or releasably attachable to thesensor electronics. For example, continuous analyte sensor 138 may beconnected to sensor electronics 112 via a sensor interposer thatmechanically and electrically interfaces the analyte sensor 138 with thesensor electronics. The sensor electronics 112, medicament delivery pump102, and/or glucose meter 104 may couple with one or more devices, suchas display devices 114, 116, 118, and/or 120.

In some example implementations, the system 100 may include acloud-based analyte processor 490 configured to analyze analyte data(and/or other patient-related data) provided via network 409 (e.g., viawired, wireless, or a combination thereof) from sensor system 101 andother devices, such as display devices 114, 116, 118, and/or 120 and thelike, associated with the host (also referred to as a patient) andgenerate reports providing high-level information, such as statistics,regarding the measured analyte over a certain time frame. A fulldiscussion of using a cloud-based analyte processing system may be foundin U.S. patent application Ser. No. 13/788,375, filed Mar. 7, 2013 andpublished as US-2013-0325352-A1, entitled “Cloud-Based Processing ofAnalyte Data”, herein incorporated by reference in its entirety. In someimplementations, one or more steps of the factory calibration orautomatic self-calibration algorithm can be performed in the cloud.

In some example implementations, the sensor electronics 112 may includeelectronic circuitry associated with measuring and processing datagenerated by the analyte sensor 138. This generated analyte sensor datamay also include algorithms, which can be used to process and calibratethe analyte sensor data, although these algorithms may be provided inother ways as well. The sensor electronics 112 may include hardware,firmware, software, or a combination thereof, to provide measurement oflevels of the analyte via an analyte sensor, such as a glucose sensor.An example implementation of the sensor electronics 112 is describedfurther below with respect to FIG. 2 .

In one implementation, the factory or self calibration algorithmsdescribed herein may be performed by the sensor electronics.

The sensor electronics 112 may, as noted, couple (e.g., wirelessly andthe like) with one or more devices, such as display devices 114, 116,118, and/or 120. The display devices 114, 116, 118, and/or 120 may beconfigured for presenting information (and/or alarming), such as sensorinformation transmitted by the sensor electronics 112 for display at thedisplay devices 114, 116, 118, and/or 120.

In one implementation, the factory or self calibration algorithmsdescribed herein may be performed at least in part by the displaydevices.

In some example implementations, the relatively small, key fob-likedisplay device 114 may comprise a wrist watch, a belt, a necklace, apendent, a piece of jewelry, an adhesive patch, a pager, a key fob, aplastic card (e.g., credit card), an identification (ID) card, and/orthe like. This small display device 114 may include a relatively smalldisplay (e.g., smaller than the large display device 116) and may beconfigured to display certain types of displayable sensor information,such as a numerical value, and an arrow, or a color code.

In some example implementations, the relatively large, hand-held displaydevice 116 may comprise a smart phone, hand-held receiver device, apalm-top computer, and/or the like. This large display device mayinclude a relatively larger display (e.g., larger than the small displaydevice 114) and may be configured to display information, such as agraphical representation of the sensor data including current andhistoric sensor data output by sensor system 100.

In some example implementations, the analyte sensor 138 may comprise aglucose sensor configured to measure glucose in the blood orinterstitial fluid using one or more measurement techniques, such asenzymatic, chemical, physical, electrochemical, spectrophotometric,polarimetric, calorimetric, iontophoretic, radiometric, immunochemical,and the like. In implementations in which the analyte sensor 138includes a glucose sensor, the glucose sensor may comprise any devicecapable of measuring the concentration of glucose and may use a varietyof techniques to measure glucose including invasive, minimally invasive,and non-invasive sensing techniques (e.g., fluorescence monitoring), toprovide data, such as a data stream, indicative of the concentration ofglucose in a host. The data stream may be sensor data (raw and/orfiltered), which may be converted into a calibrated data stream used toprovide a value of glucose to a host, such as a user, a patient, or acaretaker (e.g., a parent, a relative, a guardian, a teacher, a doctor,a nurse, or any other individual that has an interest in the wellbeingof the host). Moreover, the analyte sensor 138 may be implanted as atleast one of the following types of analyte sensors: an implantableglucose sensor, a transcutaneous glucose sensor, implanted in a hostvessel or extra corporeally, a subcutaneous sensor, a refillablesubcutaneous sensor, an intravascular sensor.

Although the disclosure herein refers to some implementations thatinclude an analyte sensor 138 comprising a glucose sensor, the analytesensor 138 may comprise other types of analyte sensors as well.Moreover, although some implementations refer to the glucose sensor asan implantable glucose sensor, other types of devices capable ofdetecting a concentration of glucose and providing an output signalrepresentative of glucose concentration may be used as well. These mayinclude, for example, fully implantable, subcutaneous, transcutaneoussensors. Furthermore, although the description herein refers to glucoseas the analyte being measured, processed, and the like, other analytesmay be used as well including, for example, ketone bodies (e.g.,acetone, acetoacetic acid and beta hydroxybutyric acid, lactate, etc.),glucagon, acetyl-CoA, triglycerides, fatty acids, intermediaries in thecitric acid cycle, choline, insulin, cortisol, testosterone, and thelike.

FIG. 2 depicts an example of electronics 12 that may be used in sensorelectronics 112 or may be implemented in a manufacturing station such asa testing station, a calibration station, a smart carrier, or otherequipment used during manufacturing of device 101, in accordance withsome example implementations. The sensor electronics 112 may includeelectronics components that are configured to process sensorinformation, such as sensor data, and generate transformed sensor dataand displayable sensor information, e.g., via a processor module. Forexample, the processor module may transform sensor data into one or moreof the following: filtered sensor data (e.g., one or more filteredanalyte concentration values), raw sensor data, calibrated sensor data(e.g., one or more calibrated analyte concentration values), rate ofchange information, trend information, rate of acceleration/decelerationinformation, sensor diagnostic information, location information,alarm/alert information, calibration information such as may bedetermined by factory or self-calibration algorithms as disclosedherein, smoothing and/or filtering algorithms of sensor data, and/or thelike.

In some embodiments, a processor module 214 is configured to achieve asubstantial portion, if not all, of the data processing, including dataprocessing pertaining to factory or self-calibration. Processor module214 may be integral to sensor electronics 12 and/or may be locatedremotely, such as in one or more of devices 114, 116, 118, and/or 120and/or cloud 490. For example, in some embodiments, processor module 214may be located at least partially within a cloud-based analyte processor490 or elsewhere in network 406.

In some example implementations, the processor module 214 may beconfigured to calibrate the sensor data, and the data storage memory 220may store the calibrated sensor data points as transformed sensor data.Moreover, the processor module 214 may be configured, in some exampleimplementations, to wirelessly receive calibration information from adisplay device, such as devices 114, 116, 118, and/or 120, to enablecalibration of the sensor data from sensor 138. Furthermore, theprocessor module 214 may be configured to perform additional algorithmicprocessing on the sensor data (e.g., calibrated and/or filtered dataand/or other sensor information), and the data storage memory 220 may beconfigured to store the transformed sensor data and/or sensor diagnosticinformation associated with the algorithms. The processor module 214 mayfurther be configured to store and use calibration informationdetermined from a factory or self-calibration, as described below.

In some example implementations, the sensor electronics 112 may comprisean application-specific integrated circuit (ASIC) 205 coupled to a userinterface 222. The ASIC 205 may further include a potentiostat 210, atelemetry module 232 for transmitting data from the sensor electronics112 to one or more devices, such as devices 114, 116, 118, and/or 120,and/or other components for signal processing and data storage (e.g.,processor module 214 and data storage memory 220). Although FIG. 1Idepicts ASIC 205, other types of circuitry may be used as well,including field programmable gate arrays (FPGA), one or moremicroprocessors configured to provide some (if not all of) theprocessing performed by the sensor electronics 12, analog circuitry,digital circuitry, or a combination thereof.

In the example depicted in FIG. 1I, through a first input port 211 forsensor data the potentiostat 210 is coupled to an analyte sensor 138,such as a glucose sensor to generate sensor data from the analyte. Thepotentiostat 210 may also provide via data line 212 a voltage to theanalyte sensor 138 to bias the sensor for measurement of a value (e.g.,a current and the like) indicative of the analyte concentration in ahost (also referred to as the analog portion of the sensor). Thepotentiostat 210 may have one or more channels depending on the numberof working electrodes at the analyte sensor 138.

In some example implementations, the potentiostat 210 may include aresistor that translates a current value from the sensor 138 into avoltage value, while in some example implementations, acurrent-to-frequency converter (not shown) may also be configured tointegrate continuously a measured current value from the sensor 138using, for example, a charge-counting device. In some exampleimplementations, an analog-to-digital converter (not shown) may digitizethe analog signal from the sensor 138 into so-called “counts” to allowprocessing by the processor module 214. The resulting counts may bedirectly related to the current measured by the potentiostat 210, whichmay be directly related to an analyte level, such as a glucose level, inthe host.

The telemetry module 232 may be operably connected to processor module214 and may provide the hardware, firmware, and/or software that enablewireless communication between the sensor electronics 112 and one ormore other devices, such as display devices, processors, network accessdevices, and the like. A variety of wireless radio technologies that canbe implemented in the telemetry module 232 include Bluetooth, BluetoothLow-Energy, ANT, ANT+, ZigBee, IEEE 802.11, IEEE 802.16, cellular radioaccess technologies, radio frequency (RF), infrared (IR), paging networkcommunication, magnetic induction, satellite data communication, spreadspectrum communication, frequency hopping communication, near fieldcommunications, and/or the like. In some example implementations, thetelemetry module 232 comprises a Bluetooth chip, although Bluetoothtechnology may also be implemented in a combination of the telemetrymodule 232 and the processor module 214.

The processor module 214 may control the processing performed by thesensor electronics 112. For example, the processor module 214 may beconfigured to process data (e.g., counts), from the sensor, filter thedata, calibrate the data, perform fail-safe checking, and/or the like.

Potentiostat 210 may measure the analyte (e.g., glucose and/or the like)at discrete time intervals or continuously.

The processor module 214 may further include a data generator (notshown) configured to generate data packages for transmission to devices,such as the display devices 114, 116, 118, and/or 120. Furthermore, theprocessor module 214 may generate data packets for transmission to theseoutside sources via telemetry module 232. In some exampleimplementations, the data packages may include an identifier code forthe sensor and/or sensor electronics 112, raw data, filtered data,calibrated data, rate of change information, trend information, errordetection or correction, and/or the like.

The processor module 214 may also include a program memory 216 and othermemory 218. The processor module 214 may be coupled to a communicationsinterface, such as a communication port 238, and a source of power, suchas a battery 234. Moreover, the battery 234 may be further coupled to abattery charger and/or regulator 236 to provide power to sensorelectronics 12 and/or charge the battery 234.

The program memory 216 may be implemented as a semi-static memory forstoring data, such as an identifier for a coupled sensor 138 (e.g., asensor identifier (ID)) and for storing code (also referred to asprogram code) to configure the ASIC 205 to perform one or more of theoperations/functions described herein. For example, the program code mayconfigure processor module 214 to process data streams or counts,filter, perform the calibration methods described below, performfail-safe checking, and the like.

The memory 218 may also be used to store information. For example, theprocessor module 214 including memory 218 may be used as the system'scache memory, where temporary storage is provided for recent sensor datareceived from the sensor. In some example implementations, the memorymay comprise memory storage components, such as read-only memory (ROM),random-access memory (RAM), dynamic-RAM, static-RAM, non-static RAM,electrically erasable programmable read only memory (EEPROM), rewritableROMs, flash memory, and the like.

The data storage memory 220 may be coupled to the processor module 214and may be configured to store a variety of sensor information. In someexample implementations, the data storage memory 220 stores one or moredays of analyte sensor data. The stored sensor information may includeone or more of the following: a time stamp, raw sensor data (one or moreraw analyte concentration values), calibrated data, filtered data,transformed sensor data, and/or any other displayable sensorinformation, calibration information (e.g., reference BG values and/orprior calibration information such as from factory calibration), sensordiagnostic information, and the like.

The user interface 222 may include a variety of interfaces, such as oneor more buttons 224, a liquid crystal display (LCD) 226, a vibrator 228,an audio transducer (e.g., speaker) 230, a backlight (not shown), and/orthe like. The components that comprise the user interface 222 mayprovide controls to interact with the user (e.g., the host).

The battery 234 may be operatively connected to the processor module 214(and possibly other components of the sensor electronics 12) and providethe necessary power for the sensor electronics 112. In otherimplementations, the receiver can be transcutaneously powered via aninductive coupling, for example.

A battery charger and/or regulator 236 may be configured to receiveenergy from an internal and/or external charger. In some exampleimplementations, the battery 234 (or batteries) is configured to becharged via an inductive and/or wireless charging pad, although anyother charging and/or power mechanism may be used as well.

One or more communication ports 238, also referred to as externalconnector(s), may be provided to allow communication with other devices,for example a PC communication (com) port can be provided to enablecommunication with systems that are separate from, or integral with, thesensor electronics 112. The communication port, for example, maycomprise a serial (e.g., universal serial bus or “USB”) communicationport, and allow for communicating with another computer system (e.g.,PC, personal digital assistant or “PDA,” server, or the like). In someexample implementations, factory information or other data may be sentto or received from the sensor, the algorithm or a cloud data source.

The one or more communication ports 238 may further include a secondinput port 237 in which calibration data may be received, and an outputport 239 which may be employed to transmit calibrated data, or data tobe calibrated, to a receiver or mobile device. FIG. 2 illustrates theseaspects schematically. It will be understood that the ports may beseparated physically, but in alternative implementations a singlecommunication port may provide the functions of both the second inputport and the output port.

In some analyte sensor systems, an on-skin portion of the sensorelectronics may be simplified to minimize complexity and/or size ofon-skin electronics, for example, providing only raw, calibrated, and/orfiltered data to a display device configured to run calibration andother algorithms required for displaying the sensor data. However, thesensor electronics 112 (e.g., via processor module 214) may beimplemented to execute prospective algorithms used to generatetransformed sensor data and/or displayable sensor information,including, for example, algorithms that: evaluate a clinicalacceptability of optional reference and/or sensor data, evaluatecalibration data for best calibration based on inclusion criteria,evaluate a quality of the calibration, compare estimated analyte valueswith time corresponding measured analyte values, analyze a variation ofestimated analyte values, evaluate a stability of the sensor and/orsensor data, detect signal artifacts (noise), replace signal artifacts,determine a rate of change and/or trend of the sensor data, performdynamic and intelligent analyte value estimation, perform diagnostics onthe sensor and/or sensor data, set modes of operation, evaluate the datafor aberrancies, and/or the like. A connected receiver or smart deviceor wearable may perform one or more of such calculations.

FIG. 3 illustrates a perspective view of an exemplary implementation ofanalyte sensor system 101 implemented as a wearable device such as anon-skin sensor assembly 600. As shown in FIG. 3 , on-skin sensorassembly includes a base 128. An adhesive 126 can couple base 128 to theskin of the host. The adhesive 126 can be an adhesive suitable for skinadhesion but not generally, e.g., foam-based adhesives.

In some embodiments, electronics unit 500 (e.g., a transmitter) may becoupled to base 128 (e.g., via mechanical interlocks such as snap fitsand/or interference features). The electronics unit 500 can includesensor electronics 112 operable to measure and/or analyze glucoseindicators sensed by glucose sensor 138. Sensor electronics 112 withinelectronics unit 500 can transmit information (e.g., measurements,analyte data, and glucose data) to a remotely located device (e.g.,114-120 shown in FIG. 1 ).

Sensor 138 may be provided as a part of a preconnected sensor thatincludes a sensor interposer. The sensor interposer (not visible in FIG.3 ) may be secured between base 128 and electronics unit 500 andelectrically coupled to electronics unit 500 to couple sensor 138 to thesensor electronics (e.g., sensor electronics 112 of FIG. 1 ).

FIG. 4 shows a schematic illustration of a preconnected sensor 400. Asshown in FIG. 4 , preconnected sensor 400 includes sensor interposer 402permanently attached to sensor 138. In the example of FIG. 4 , sensorinterposer 402 includes substrate 404, first contact 406, and secondcontact 408. Contact 406 is electrically coupled to a first contact on aproximal end of sensor 138 and contact 408 is electrically coupled to asecond contact on the proximal end of sensor 138. The distal end ofsensor 138 is a free end configured for insertion into the skin of thehost.

As shown in FIG. 4 , contact 406 is coupled to an external contact 410and contact 408 is coupled to an external contact 412. As described infurther detail hereinafter, external contacts 410 and 412 are sized,shaped, and positioned to electrically interface with sensor electronics112 in electronics unit 500 in addition to electrically interfacing withprocessing circuitry of manufacturing equipment such one or more testingstations and/or one or more calibration stations. Although variousexamples are described herein in which two contacts 410 and 412 on theinterposer are coupled to two corresponding contacts 406 and 408 onsensor 138, this is merely illustrative. In other implementations,interposer 402 and sensor 138 may each be provided with a single contactor may each be provided with more than two contacts. In someimplementations, interposer 402 and sensor 138 may have a same number ofcontacts. In some implementations, interposer 402 and sensor 138 mayhave a different number of contacts. For example, in someimplementations, interposer 402 may have additional contacts forcoupling to or between various components of a manufacturing station.

Substrate 404 may be sized and shaped to mechanically interface withbase 128 and/or electronics unit 500 in addition to mechanicallyinterfacing with manufacturing equipment such one or more assemblyequipment, testing stations and/or one or more calibration stations.Interposer 402 may be attached and/or electrically coupled to sensor138. Interposer 402 may be attached to sensor 138 using, as examples,adhesive, spring contacts, wrapped flexible circuitry, a conductiveelastomer, a barrel connector, a molded interconnect device structure,magnets, anisotropic conductive films, or other suitable structures ormaterials for mechanically and electrically attaching interposer 402 tosensor 138 before or during assembly, manufacturing, testing and/orcalibration operations. Interposer 402 may be attached to sensor 138using, as examples, spot welding, swaging, crimping, clipping, solderingor brazing, plastic welding, overmolding, or other suitable methods formechanically and electrically attaching interposer 402 to sensor 138before or during assembly, manufacturing, testing and/or calibrationoperations. Substrate 404 may include datum features (sometimes referredto as datum structures) such as a recess, an opening, a surface or aprotrusion for aligning, positioning, and orienting sensor 138 relativeto interposer 402. Substrate 404 may also include, or may itself form,one or more anchoring features for securing and aligning the analytesensor during manufacturing (e.g., relative to a manufacturing station).

FIG. 5 shows a block diagram of an exemplary system 5000 havingmanufacturing equipment such as one or more manufacturing stations 5091,one or more positioning or testing stations 5002 and/or one morecalibration stations 5004, and having an on-skin sensor assembly 600,each configured to receive sensor interposer 402 and to communicativelycouple to sensor 138 via sensor interposer 402.

System 5000 may include one or more positioning or testing stations 5002having processing circuitry 5012 configured to perform testingoperations with sensor 138 to determine parameters and/or to verify theoperational integrity of sensor 138. Testing operations may includeverifying electrical properties of a sensor 138, verifying communicationbetween a working electrode and contact 408, verifying communicationbetween a reference electrode or additional electrodes and contact 406,and/or other electronic verification operations for sensor 138.Processing circuitry 5012 may be communicatively coupled with sensor 138for testing operations by inserting substrate 404 into a receptacle 5006(e.g., a recess in a housing of testing station 5002) until contact 410is coupled to contact 5010 of testing station 5002 and contact 412 iscoupled to contact 5008 of testing station 5002.

System 5000 may include one or more calibration stations 5004 havingprocessing circuitry 5020 configured to perform calibration operationswith sensor 138 to obtain calibration data for in vivo operation ofsensor 138. Calibration data obtained by calibration equipment 5004 maybe provided to on-skin sensor assembly 600 to be used during operationof sensor 138 in vivo. Processing circuitry 5020 may be communicativelycoupled with sensor 138 for calibration operations by insertingsubstrate 404 into a receptacle 5014 (e.g., a recess in a housing ofcalibration station 5004) until contact 410 is coupled to contact 5018of testing station 5002 and contact 412 is coupled to contact 5016 oftesting station 5002.

System 5000 may include one or more manufacturing stations 5091.Manufacturing station 5091 may also serve in providing the functions ofa testing station as described herein, a calibration station asdescribed herein, or another manufacturing station. Manufacturingstation 5091 may include processing circuitry 5092 and/or mechanicalcomponents 5094 operable to perform testing operations, calibrationoperations, and/or other manufacturing operations such as sensorstraightening operations, membrane application operations, bakingoperations, calibration-check operations, glucose sensitivity operations(e.g., sensitivity slope, baseline, and/or noise calibrationoperations), and/or visual inspection operations. Manufacturingparameters that may be measured during these various operations mayinclude, by way of illustration, temperature, humidity, the content(e.g., PVP, ethanol, etc.) of the particular coating solution in whichthe sensor is dipped (which may be determined from the refractive indexof the solution), the duration of the dip, the number of times thesensors are dipped in the solution, and the duration, temperature andhumidity of the curing process.

In the example of FIG. 5 , testing station 5002 and calibration station5004 include receptacles 5006 and 5014. However, this is merelyillustrative and interposer 402 may be mounted to testing station 5002and calibration station 5004 and/or manufacturing station 5091 usingother mounting features such as grasping, clipping, or clamping figures.For example, manufacturing station 5091 includes grasping structures5093 and 5095, at least one of which is movable to grasp interposer 402(or a carrier having multiple interposers and sensors). Structure 5093may be a stationary feature having one or more electrical contacts suchas contact 5008. Structure 5095 may be a movable feature that moves(e.g., slides in a direction 5097) to grasp and secure interposer 402 inan electrically coupled position for manufacturing station 5091. Inother implementations, both features 5093 and 5095 are movable.

Sensor interposer 402 may also include an identifier 450 (see, e.g.,FIG. 4 ). Identifier 450 may be formed on or embedded within substrate404. Identifier 450 may be implemented as a visual or optical identifier(e.g., a barcode pre-printed or printed on-the-fly on substrate 404 oretched in to substrate 404), a radio frequency (RF) identifier, or anelectrical identifier (e.g., a laser-trimmed resistor, a capacitiveidentifier, an inductive identifier, or a micro storage circuit (e.g.,an integrated circuit or other circuitry in which the identifier isencoded in memory of the identifier) programmable with an identifierand/or other data before, during, or after testing and calibration).Identifier 450 may be used for tracking each sensor through themanufacturing process for that sensor (e.g., by storing a history oftesting and/or calibration data for each sensor). For example,identifier 450 may be used for binning of testing and calibrationperformance data. Identifier 450 may be a discrete raw value or mayencode information in addition to an identification number. Identifier450 may be used for digitally storing data in non-volatile memory onsubstrate 404 or as a reference number for storing data external tointerposer 402.

Testing station 5002 may include a reader 5011 (e.g., an optical sensor,an RF sensor, or an electrical interface such as an integrated circuitinterface) that reads identifier 450 to obtain a unique identifier ofsensor 138. Testing data obtained by testing station 5002 may be storedand/or transmitted along with the identifier of sensor 138.

Calibration station 5004 may include a reader 5011 (e.g., an opticalsensor, an RF sensor, or an electrical interface) that reads identifier450 to obtain a unique identifier of sensor 138. Calibration dataobtained by calibration station 5004 may be stored and/or transmittedalong with the identifier of sensor 138. In some implementations,calibration data obtained by calibration station 5004 may be added toidentifier 450 by calibration station 5004 (e.g., by programming thecalibration data into the identifier). In some implementations,calibration data obtained by calibration station 5004 may be transmittedto a remote system or device along with identifier 450 by calibrationstation.

As shown in FIG. 5 , on-skin sensor assembly 600 may include one or morecontacts such as contact 5022 configured to couple electronics unit 500to contacts 410 and 412 of interposer 402 and thus to sensor 138.Interposer 402 may be sized and shaped to be secured within a cavity5024 between base 128 and electronics unit 500 such that sensor 138 iscoupled to electronics unit 500 via interposer 402, identifier 450 isaccessible by reader 5013, and sensor 138 is positionally secured toextend through opening 180 for insertion for in vivo operations.

Although one calibration station and one testing station are shown inFIG. 5 , it should be appreciated that more than one testing stationand/or more than one calibration station may be included in system 5000.Although calibration station 5004 and testing station 5002 are shown asseparate stations in FIG. 5 , it should be appreciated that, in someimplementations calibration stations and testing stations may becombined into one or more calibration/testing stations (e.g., stationsin which processing circuitry for performing testing and calibrationoperations is provided within a common housing and coupled to a singleinterface 5006). In addition, data from one or more manufacturingstations may be compiled and stored in and/or stored and associated withthe sensor and interposer.

On-skin sensor assembly 600 may also include a reader 5013 (e.g., anoptical sensor, an RF sensor, or an electrical interface) that readsidentifier 450 to obtain a unique identifier of sensor 138. Sensorelectronics in electronics unit 500 may obtain calibration data for invivo operation of sensor 138 based on the read identifier 450. Thecalibration data may be stored in, and obtained, from identifier 450itself, or identifier 450 may be used to obtain the calibration data forthe installed sensor 138 from a remote system such as a cloud-basedsystem.

Additional details concerning the example of the sensor system shownFIGS. 1-5 may be found in U.S. Pat. Appl. Ser. No. 62/576,560, filedOct. 24, 2017, entitled “Preconnected Analyte Sensors,” which is herebyincorporated by reference in its entirety.

Determination of Sensor Sensitivity

As described elsewhere herein, in certain embodiments, self-calibrationof the analyte sensor system can be performed by determining sensorsensitivity based on a sensitivity profile (and a measured or estimatedbaseline), so that the following equation can be solved:y=mx+bwherein y represents the sensor signal (counts), x represents theestimated glucose concentration (mg/dL), m represents the sensorsensitivity to the analyte (counts/mg/dL), and b represents the baselinesignal (counts). From this equation, a conversion function can beformed, whereby a sensor signal is converted into an estimated glucoseconcentration.

It has been found that a sensor's sensitivity to analyte concentrationduring a sensor session will often change or drift as a function oftime. FIG. 6 illustrates this phenomenon and provides a plot of sensorsensitivities 110 of a group of continuous glucose sensors as a functionof time during a sensor session. FIG. 7 provides three plots ofconversion functions at three different time periods of a sensorsession. As shown in FIG. 7 , the three conversion functions havedifferent slopes, each of which correspond to a different sensorsensitivity. Accordingly, the differences in slopes over time illustratethat changes or drift in sensor sensitivity occur over a sensor session.

Referring back to the study associated with FIG. 6 , the sensors weremade in substantially the same way under substantially the sameconditions. The sensor sensitivities associated with the y-axis of theplot are expressed as a percentage of a substantially steady statesensitivity that was reached about three days after start of the sensorsession. In addition, these sensor sensitivities correspond tomeasurements obtained from YSI tests. As shown in the plot, thesensitivities (expressed as a percentage of a steady state sensitivity)of each sensor, as measured, are very close to sensitivities of othersensors in the group at any given time of the sensor session. While notwishing to be bound by theory, it is believed that the observed upwardtrend in sensitivity (over time), which is particularly pronounced inthe early part of the sensor session, can be attributed to conditioningand hydration of sensing regions of the working electrode. It is alsobelieved that the glucose concentration of the fluid surrounding thecontinuous glucose sensor during startup of the sensor can also affectthe sensitivity drift.

With the sensors tested in this study, the change in sensor sensitivity(expressed as a percentage of a substantially steady state sensitivity),over a time defined by a sensor session, resembled a logarithmic growthcurve. It should be understood that other continuous analyte sensorsfabricated with different techniques, with different specifications(e.g., different membrane thickness or composition), or under differentmanufacturing conditions, may exhibit a different sensor sensitivityprofile (e.g., one associated with a linear function). Nonetheless, withimproved control over operating conditions of the sensor fabricationprocess, high levels of reproducibility have been achieved, such thatsensitivity profiles exhibited by individual sensors of a sensorpopulation (e.g., a sensor lot) are substantially similar and sometimesnearly identical.

It has been discovered that the change or drift in sensitivity over asensor session is not only substantially consistent among sensorsmanufactured in substantially the same way under substantially sameconditions, but also that modeling can be performed through mathematicalfunctions that can accurately estimate this change or drift. Asillustrated in FIG. 6 , an estimative algorithm function 120 can be usedto define the relationship between time during the sensor session andsensor sensitivity. The estimative algorithm function may be generatedby testing a sample set (comprising one or more sensors) from a sensorlot under in vivo and/or in vitro conditions. Alternatively, theestimative algorithm function may be generated by testing each sensorunder in vivo and/or in vitro conditions.

In some embodiments, a sensor may undergo an in vitro sensor sensitivitydrift test, in which the sensor is exposed to changing conditions (e.g.,step changes of glucose concentrations in a solution), and an in vitrosensitivity profile of the sensor is generated over a certain timeperiod. The time period of the test may substantially match an entiresensor session of a corresponding in vivo sensor, or it may encompass aportion of the sensor session (e.g., the first day, the first two days,or the first three days of the sensor session, etc.). It is contemplatedthat the above-described test may be performed on each individualsensor, or alternatively on one or more sample sensors of a sensor lot.From this test, an in vitro sensitivity profile may be created, fromwhich an in vivo sensitivity profile may be modeled and/or formed.

From the in vivo or in vitro testing, one or more data sets, eachcomprising data points associating sensitivity with time, may begenerated and plotted. A sensitivity profile or curve can then be fittedto the data points. If the curve fit is determined to be satisfactory(e.g., if the standard deviation of the generated data points is less acertain threshold), then the sensor sensitivity profile or curve may bejudged to have passed a quality control and suitable for release. Fromthere, the sensor sensitivity profile can be transformed into anestimative algorithm function or alternatively into a look-up table. Thealgorithm function or look-up table can be stored in a computer-readablememory, for example, and accessed by a computer processor.

The estimative algorithm function may be formed by applying curvefitting techniques that regressively fit a curve to data points byadjusting the function (e.g., by adjusting constants of the function)until an optimal fit to the available data points is obtained. Simplyput, a “curve” (i.e., a function sometimes referred to as a “model”) isfitted and generated that relates one data value to one or more otherdata values and selecting parameters of the curve such that the curveestimates the relationship between the data values. By way of example,selection of the parameters of the curve may involve selection ofcoefficients of a polynomial function. In some embodiments, the curvefitting process may involve evaluating how closely the curve determinedin the curve fitting process estimates the relationship between the datavalues, to determine the optimal fit. The term “curve,” as used herein,is a broad term, and is to be given its ordinary and customary meaningto a person of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and refers to a function or a graph of afunction, which can involve a rounded curve or a straight curve, i.e., aline.

The curve may be formed by any of a variety of curve fitting techniques,such as, for example, the linear least squares fitting method, thenon-linear least squares fitting method, the Nelder-Mead Simplex method,the Levenberg-Marquardt method, and variations thereof. In addition, thecurve may be fitted using any of a variety of functions, including, butnot limited to, a linear function (including a constant function),logarithmic function, quadratic function, cubic function, square rootfunction, power function, polynomial function, rational function,exponential function, sinusoidal function, and variations andcombinations thereof. For example, in some embodiments, the estimativealgorithm comprises a linear function component which is accorded afirst weight w1, a logarithmic function component which is accorded asecond weight w2, and an exponential function component which isaccorded a third weight w3. In further embodiments, the weightsassociated with each component can vary as a function of time and/orother parameters, but in alternative embodiment, one or more of theseweights are constant as a function of time.

In certain embodiments, the estimative algorithm function's correlation(e.g., R2 value), which is a measure of the quality of the fit of thecurve to the data points, with respect to data obtained from the samplesensors, may be one metric used to determine whether a function isoptimal. In certain embodiments, the estimative algorithm functionformed from the curve fitting analysis may be adjusted to account forother parameters, e.g., other parameters that may affect sensorsensitivity or provide additional information about sensor sensitivity.For example, the estimative algorithm function may be adjusted toaccount for the sensitivity of the sensor to hydrogen peroxide or otherchemical species.

Estimative algorithms formed and used to accurately estimate anindividual sensor's sensitivity, at any time during a sensor session,can be based on factory calibration and/or based on a single earlyreference measurement (e.g., using a single point blood glucosemonitor). In some embodiments, sensors across a population of continuousanalyte sensors manufactured in substantially the same way undersubstantially same conditions exhibit a substantially fixed in vivo toin vitro sensitivity relationship. For example, in one embodiment, thein vivo sensitivity of a sensor at a certain time after start of sensoruse (e.g., at t=about 5, 10, 15, 30, 60, 120, or 180 minutes aftersensor use) is consistently equal to a measured in vitro sensitivity ofthe sensor or of an equivalent sensor. From this relationship, aninitial value of in vivo sensitivity can be generated, from which analgorithmic function corresponding to the sensor sensitivity profile canbe formed. Put another way, from this initial value (which representsone point in the sensor sensitivity profile), the rest of the entiresensor sensitivity profile can be determined and plotted. The initialvalue of in vivo sensitivity can be associated with any portion of thesensor sensitivity profile. In certain embodiments, multiple initialvalues of in vivo sensitivities, which are time-spaced apart, and whichcorrespond to multiple in vitro sensitivities, can be calculated andcombined together to generate the sensor sensitivity profile.

With some embodiments, it has been found that not only does the sensor'ssensitivity tend to drift over time, but that the sensor's baseline alsodrifts over time. Accordingly, in certain embodiments, the conceptsbehind the methods and systems used to predict sensitivity drift canalso be applied to create a model that predicts baseline drift overtime. Although not wishing to be bound by theory, it is believed thatthe total signal received by the sensor electrode is comprised of aglucose signal component, an interference signal component, and aelectrode-related baseline signal component that is a function of theelectrode and that is substantially independent of the environment(e.g., extracellular matrix) surrounding the electrode. As noted above,the term “baseline,” as used herein, refers without limitation to thecomponent of an analyte sensor signal that is not related to the analyteconcentration. Accordingly, the baseline, as the term is defined herein,is comprised of the interference signal component and theelectrode-related baseline signal component. Again, while not wishing tobe bound by theory, it is believed that increased membrane permeabilitytypically not only results in an increased rate of glucose diffusionacross the sensor membrane, but also in an increased rate of diffusionof interferents across the sensor membrane. Accordingly, changes insensor membrane permeability over time, which causes sensor sensitivitydrift, will similarly also likely cause the interference signalcomponent of the baseline to drift. Simply put, the interference signalcomponent of the baseline is not static, and is typically changing as afunction of time, which, in turn, causes the baseline to also drift overtime. By analyzing how each of the aforementioned components of thebaseline reacts to changing conditions and to time (e.g., as a functionof time, temperature), a predictive model can be developed to predicthow the baseline of a sensor will drift during a sensor session. Bybeing able to prospectively predict both sensitivity and baseline of thesensor, it is believed that a factory calibrated or automaticallyself-calibrating continuous analyte sensor can be achieved, i.e., asensor that does not require use of reference measurements (e.g., afingerstick measurement) for calibration.

Calibration Code

The process of manufacturing continuous analyte sensors may sometimes besubjected to a degree of variability between sensor lots, as will bedescribed in greater detail below. To compensate for this variability,one or more calibration codes may be assigned to each sensor or sensorset to define parameters that can affect sensor sensitivity or provideadditional information about the sensitivity profile. The calibrationcodes can reduce variability in the different sensors, ensuring that theresults obtained from using sensors from different sensors lots will begenerally equal and consistent by applying an algorithm that adjusts forthe differences. In one embodiment, the analyte sensor system may beconfigured such that one or more calibration codes are to be manuallyentered into the system by a user. In other embodiments, the calibrationcodes may be part of a calibration encoded label that is adhered to (orinserted into) a package of multiple sensors. The calibration encodedlabel itself may be read or interrogated by any of a variety oftechniques, including, but not limited to, optical techniques, RFID(radio-frequency identification), or the like, and combinations thereof.These techniques for transferring the code to the sensor system may bemore automatic, accurate, and convenient for the patient, and less proneto error, as compared to manual entry. Manual entry, for instance,possesses the inherent risk of an error caused by a patient or hospitalstaff entering the wrong code, which can lead to an incorrectcalibration, and thus inaccurate glucose concentration readings. Inturn, this may result in a patient or hospital staff taking aninappropriate action (e.g., injecting insulin while in a hypoglycemicstate).

In some embodiments, calibration codes assigned to a sensor may includea first calibration code associated with a predetermined logarithmicfunction corresponding to a sensitivity profile, a second calibrationcode associated with an initial in vivo sensitivity value, and othercalibration codes, with each code defining a parameter that affectssensor sensitivity or provides information about sensor sensitivity. Theother calibration codes may be associated with any a priori informationor parameter described elsewhere herein and/or any parameter that helpsdefine a mathematical relationship between the measured signal andanalyte concentration. The calibration code may be developed from thesemeasurements or may be developed based on manufacturing parametersknown, determined, or measured during fabrication of, e.g., a lot, or bya combination of these.

In some embodiments, the package used to store and transport acontinuous analyte sensor (or sensor set) may include detectorsconfigured to measure certain parameters that may affect sensorsensitivity or provide additional information about sensor sensitivityor other sensor characteristics. For example, in one embodiment, thesensor package may include a temperature detector configured to providecalibration information relating to whether the sensor has been exposedto a temperature state greater than (and/or less than) one or morepredetermined temperature values. In some embodiments, the one or morepredetermined temperature value may be greater than about 75° F.,greater than about 80° F., greater than about 85° F., greater than about90° F., greater than about 95° F., greater than about 100° F., greaterthan about 105° F., and/or greater than about 110° F. Additionally oralternatively, the one or more predetermined temperature value may beless than about 75° F., less than about 70° F., less than about 60° F.,less than about 55° F., less than about 40° F., less than about 32° F.,less than about 10° F., and/or less than about 0° F. In certainembodiments, the sensor package may include a humidity exposureindicator configured to provide calibration information relating towhether the sensor has been exposed to humidity levels greater than orless than one or more predetermined humidity values. In someembodiments, the one or more predetermined humidity value may be greaterthan about 60% relative humidity, greater than about 70% relativehumidity, greater than about 80% relative humidity, and/or greater thanabout 90% relative humidity. Alternatively or additionally, the one ormore predetermined humidity value may be less than about 30% relativehumidity, less than about 20% relative humidity, and/or less than about10% relative humidity.

Upon detection of exposure of the sensor to certain levels oftemperature and/or humidity, a corresponding calibration code may bechanged to account for possible effects of this exposure on sensorsensitivity or other sensor characteristics. This calibration codechange may be automatically performed by a control system associatedwith the sensor package. Alternatively, in other embodiments, anindicator (e.g., a color indicator) that is adapted to undergo a change(e.g., a change in color) upon exposure to certain environments may beused. By way of example and not to be limiting, the sensor package mayinclude an indicator that irreversibly changes color from a blue colorto a red color, upon exposure of the package to a temperature greaterthan about 85° F., and also include instructions to the user to enter acertain calibration code when the indicator has a red color. Althoughexposure to temperature and humidity are described herein as examples ofconditions that may be detected by the sensor package, and used toactivate a change in calibration code information, it should beunderstood that other conditions may also be detected and used toactivate a change in calibration code information.

In certain embodiments, the continuous analyte system may comprise alibrary of stored sensor sensitivity functions or calibration functionsassociated with one or more calibration codes. Each sensitivity functionor calibration function results in calibrating the system for adifferent set of conditions. Different conditions during sensor use maybe associated with temperature, body mass index, and any of a variety ofconditions or parameters that may affect sensor sensitivity or provideadditional information about sensor sensitivity. The library can alsoinclude sensitivity profiles or calibrations for different types ofsensors or different sensor lots. For example, a single sensitivityprofile library can include sub-libraries of sensitivity profiles fordifferent sensors made from different sensor lots and/or made withdifferent design configurations (e.g., different design configurationscustomized for patients with different body mass index).

Advanced and/or Multivariate Calibration

As previously mentioned, the sensitivity of an analyte sensor may changeover time as a result of a variety of different manufacturing andenvironmental parameters. In some embodiments some of these parametersinvolve information that can be obtained during distinct phases of theanalyte sensor lifecycle. As shown in FIGS. 8A and 8B, in certainembodiments these different phases may include one or more of thefollowing: a sensor manufacturing phase 5702, a sensor packaging phase5704, a sensor package sterilization phase 5706 in which the sensor issterilized while in the package (using, e.g., any suitable sterilizationgas, which may include conventional sterilization gases or,alternatively, nitrogen dioxide, chlorine dioxide or ethylene oxide, oralternatively, using an e-beam, at least to sterilize the transmitter,whose interior may be shielded to deflect the e-beam), a sensor shippingphase 5708, a sensor storage phase 5710 (e.g., in a warehouse, retailenvironment, user premises), a sensor startup/insertion phase 5712during which the sensor is placed in vivo, and a sensor in vivo phase5714 in which the sensor is operational while in vivo. Of course, inother embodiments, the analyte sensor lifecycle may be divided intodifferent lifecycles. FIGS. 8A-8B further show examples of environmentaland other factors that may be monitored during each lifecycle phase andwhich may be taken into account when calibrating the sensor.

Moreover, the various lifecycle phases enumerated above in some casesmay be further divided into identifiable sub-stages. For instance, insome embodiments the sensor manufacturing phase may include a sub-phasein which the analyte sensor may be preconnected to one or morecomponents of the sensor electronics or even all of the sensorelectronics. If the analyte sensor system employs a sensor interfacesuch as the sensor interposer 402 shown in the embodiment of FIG. 4 ,then the sensor may be preconnected to the sensor interface, andpossibly one or more components of the sensor electronics as well.Alternatively, instead of treating the pre-connection process as part ofthe sensor manufacturing phase, it may be identified as a separate phasethat occurs before or after the sensor manufacturing phase.

The sensor manufacturing phase may be further divided into a wirecutting phase, a wire coating phase, a wire baking phase, a wire skivingphase, and so on.

In addition to, or instead of the sensor interposer, the components ofthe sensor electronics that may be preconnected to the analyte sensormay include a processor (e.g., processor module 214 in the embodiment ofFIG. 2 ), a memory (e.g., data storage memory 220 in the embodiment ofFIG. 2 ), a potentiostat (e.g., potentiostat 210 in the embodiment ofFIG. 2 ), an analog measurement circuit, a digital measurement circuit,and/or a transmitter (e.g., telemetry module 232 in the embodiment ofFIG. 2 ).

FIG. 9 shows a schematic block diagram of one particular example of apreconnected analyte sensor system that includes an analyte sensor 5602,a sensor interconnection module 5604 (e.g., the sensor interposer) andmeasurement electronics 5608. The measure electronics 5608 include apotentiostat 5610 and a number of optional components. The optionalcomponents may include any one or more of the following: a temperaturemeasurement circuit 5612, an impedance measurement circuit 5614, aprocessor 5616, a radio 5618, a humidity measurement circuit 5620, apressure measurement circuit 5622, a motion detector circuit 5624, acapacitive measurement circuit 5626, a display/status indicator 5628, adata storage 5630, a power source 5632 and a clock 5634.

FIG. 9 also shows potential sources of error 5640 and 5650 that may bereduced or eliminated by using a preconnected analyte sensor system.These sources of error may include, for example, errors that may arisewhen a user is required to connect the sensor to the transmitter orother electronics such as the contact resistance, connection stability,electronic noise and environmental factors. In addition, FIG. 9 showsvarious errors in the measurement electronics that may be reduced oreliminated by use of a preconnected analyte sensor system. These errorsmay include, for example, the tolerance of the electronic components,leakage current, measurement error, resolution error, electronic noiseand environmental factors that impact the electronics.

FIG. 10 is a Monte Carlo simulation of 5000 samples using a randomlyselected number within the statistical distribution of the inputvariables that compares a non-preconnected system and a preconnectedsystem. This shows the number of samples falling within the 10 mg/dL or10% glucose concentration error target. It shows a reduction of thestatistical distribution in error that can be achieved with apreconnected system versus a non-preconnected system in which a varietyof individual components having a distribution in their characteristics(e.g., gain, offset, contact, etc.) are combined into a system.

The comparison uses a unitless measurement of current (counts) andcalibrates to a known glucose calibration solution. This is an exemplarymodel and not all variables that affect the system are taken intoaccount. The variation induced by component and measurement variationsare eliminated. In particular the values of gain and offset are notmeasured and calibrated to a unit value so their induced error iseliminated. The system is calibrated with the exact components thatinfluence the values of contact resistance, leakage current and biasvoltage. Therefore, their variability is eliminated and they can bemodeled as fixed values.

In some embodiments some of the electronics may be incorporated in theinterposer or other interconnect component that is connected to thesensor. In this way by pre-connecting the interposer to the sensor, someor all of the electronics will also be preconnected to the sensor. Thiswould allow calibration and other data to be conveniently stored duringthe manufacturing process. In some embodiments the interposer (or othercomponent connected to the sensor) may be used for other purposes aswell. For instance, it may be used to store a code that can be used totrack the sensor during manufacturing and/or other life phases of thesensor. The code may be embodied, for instance, in a series of resistorsthat are printed on an interposer or the like. The code may beprogrammed by laser cutting selected traces to impart a final resistanceto or on the printed resistor. The resistance may be read out by thetransmitter when the transmitter is installed via spring contacts or thelike on the interposer.

In some embodiments any of these or other components that may bepreconnected to the analyte sensor may be configured so that theconnection is maintained through multiple periods during a sensorlifecycle. In this way the pre-connection may be maintained throughoutthe entirety or multiple sequences of the analyte sensor's lifecycle.Hence, the system-level calibrations (i.e., the analyte sensor and thepreconnected components of the sensor electronics) that are performedover the analyte sensor's and/or sensor electronics lifecycle shouldcorrelate to changes in the system during one or more phases.

The components to which analyte sensor is preconnected may be packagedalong with the analyte sensor in the sterile package that is used toship and store the analyte sensor. Accordingly, in these embodiments itmay be advantageous if the preconnected components are single-use,disposable components.

Parameters that may uniquely impact the analyte sensor sensitivityduring the manufacturing phase may include, without limitation,parameters such as the materials used to fabricate sensor membrane, thethickness of the sensor membrane, the temperature at which the sensormembrane was cured, the length of time the sensor was exposed to aparticular coating solution, the enzyme activity level, amount ofcoating applied, etc. Parameters that may uniquely impact the analytesensor sensitivity during the packaging phase may include, withoutlimitation, the sterilization dosage, sterilization method, enzymeactivity, packaging material, etc. Additional parameters that may impactthe analyte sensor sensitivity during any and all phases may includevarious environmental parameters such as temperature and humidity andthe duration of time at which the sensor was exposed to the measuredvalues of temperature and humidity, for example.

In some embodiments the analyte sensor may be calibrated based onmeasurements of one or more of the various parameters that impact theanalyte sensor sensitivity during two or more phases of the analytesensor lifecycle. An illustrative calibration process 2400 in accordancewith some embodiments will now be discussed with reference to FIG. 11 .The calibration process may be performed by the sensor electronics inthe analyte monitoring system, without user intervention, therebyavoiding the need for external user calibration when the device is inuse. The process begins at block 2402 when the analyte sensor ispreconnected to one or more components of the sensor electronics during,e.g., a manufacturing phase. Next, at block 2404, the analyte sensor,along with the preconnected sensor electronic component(s), undergoes aninitial calibration process. The initial calibration process may use anyavailable a priori information comprising sensor sensitivity informationin order to obtain a calibration factor that can be used to convertsensor data (e.g., in units of current or counts) into estimated analytevalues (e.g., in unit of analyte concentration).

A number of advantages may arise from performing the initial calibrationprocess after the analyte sensor has been preconnected to the one ormore components of the sensor electronics. Measurements may be takenduring the manufacturing process phase to establish reference values forcomparison at a later time period. These reference values may be used bya processing algorithm to quantify scale and offset values from a knownstate. In some cases the reference measurement value is dependent on asensor characteristic that is influenced by a connection property. Thismay enable a measurement to be taken that would not be possible in aseparable system. For instance, errors that may separately arise in theanalyte sensor and the sensor electronics may be reduced or eliminatedby calibrating them as a single unit. In addition, errors that may arisefrom the act of connecting (and disconnecting) the analyte sensor to thesensor electronics can also be reduced or eliminated. For example, theimpedance measurement of the sensor may be more stable if the sensorremains continuously connected to the sensor electronics.

After preconnecting the analyte sensor to one or more components of thesensor electronics, the calibration process 2400 proceeds to block 2406where one or more environmental parameters affecting sensor sensitivityare monitored during one or more phases of the analyte sensor lifecyclesubsequent to the manufacturing phase. For instance, environmentalparameters may be monitored during the sensor packaging stage, sensorpackage sterilization phase, sensor shipping stage, sensor storagestage, sensor insertion stage and/or the sensor use stage.

The monitoring of the environmental parameters may be accomplished inany number of different ways. For instance, if the analyte sensor ispreconnected to at least the components of the sensor electronics thatare used to apply a stimulus signal to an analyte sensor and measure asignal response to the stimulus signal, the signal response can be usedto determine an impedance value of the analyte sensor. Varioustechniques for calculating analyte sensor impedance values based on thesignal response are described elsewhere herein, such as one or more ofthe techniques discussed in U.S. patent application Ser. No. 14/144,343,published as US-2014-0114156-A1 and entitled “Advanced Analyte SensorCalibration and Error Detection,” which is hereby incorporated byreference in its entirety. The determined impedance may then be comparedto a pre-established impedance-to-environmental parameter relationshipsuch as a pre-established impedance-to-temperature relationship, apre-established impedance-to-humidity relationship, or a pre-establishedimpedance-to-membrane damage relationship, as also discussed in theaforementioned patent document. In this way, the environmentalparameter(s) may be monitored.

In an alternative embodiment, the environmental parameters may bemonitored using an environmental sensor such as a temperature monitor ora humidity monitor. For instance, such monitors may be incorporated intothe sterile package in which the analyte sensor is stored when it leavesthe factory. Alternatively, the monitors, such as the temperaturemonitor, may be directly incorporated into the sensor electronicsthemselves. In some cases the monitors need not provide a numericalvalue for the environmental parameters, but may simply indicate if theenvironmental parameter has fallen outside of specified ranges withinwhich the sensitivity of the analyte sensor is known to remainrelatively stable. In this way a relatively simple environmental monitormay be employed.

Once the manufacturing and environmental parameter(s) has been obtained,the calibration process 2400 proceeds to block 2408 where an updatedcalibration factor is determined based on a pre-establishedenvironmental and/or manufacturing parameters-to-analyte sensorsensitivity relationship. In determining the updated calibrated factor,information in addition to the measured parameter(s) may be taken intoaccount. For instance, the initial calibration factor may be used aswell. The updated calibration factor may be used to properly calibratethe analyte sensor so that sensor data (e.g., in units of current orcounts) can be converted into analyte values (in units of analyteconcentration).

The updated calibration factor may be determined at any suitable timeafter the environmental parameters have been obtained. In part, thiswill depend on the particular components of the sensor electronics thathave been preconnected to the analyte sensor. For example, if thepreconnected components include a suitable processor and associatedmemory and a power source (e.g., a battery), then the updatedcalibration factor may be determined as soon as the environmentalparameters are obtained e.g., while in the sterile package or while instorage. Alternatively, if such a processor is not available, theenvironmental parameters may be stored in one of the preconnectedcomponents and communicated when the remainder of the sensor electronicsare connected, such as when the sensor insertion phase is initiated.Alternatively, if the preconnected components also include atransmitter, then the environmental parameters may be transmitted to theremainder of the sensor electronics or to another connected device.

In some embodiments the updated calibration parameter may be determinedby a processor and associated algorithms that are not incorporated inthe sensor electronics. Rather, the updated calibration parameter may bestored in the preconnected electronic components and uploaded at asuitable time to a device which with the sensor electronics communicates(e.g., display devices 114, 116, 118 and/or 120 in FIG. 1 ) or to acloud-based processor (e.g. cloud-based analyte processor 490 in FIG. 1). The cloud-based processor or other device that calculates the updatedcalibration parameter then downloads it to the sensor electronics foruse in calibrating the analyte sensor.

As previously mentioned, environmental parameters may be obtained atmultiple times during the various phases of the analyte sensor lifecycleand even at multiple times during a single phase (e.g., storage). Theenvironmental parameters obtained at each of these different times maythen be used, potentially in combination with other factors (e.g. lotfactors, in vivo measured values, cloud data, time since sensormanufacture, individual patient factors) to determine a final updatedcalibration factor.

In some embodiments instead of, or in addition to, periodically updatingthe calibration factor at multiple time during the various lifecyclephases, a single complex adaptive calibration factor may be generatedduring the sensor use phase. The complex adaptive calibration factor maycombine an initial calibration factor obtained during sensor manufacturewith environmental conditions experienced by the analyte sensor (and anypreconnected electronics, if present) from sensor manufacture to sensorinsertion. In this way the experience of the analyte sensor during itslifetime is encoded in a form that allows it to be used by thecalibration algorithm. Thus, instead of separately accounting for eachindividual environmental parameter such as temperature and humidity andsensor characteristics such as impedance, a single encoded value orprofile may be provided to the calibration algorithm that encapsulatesall the manufacturing and/or environmental parameters and sensorcharacteristics.

FIGS. 12(a)-12(c) are timelines showing various phases over the lifetimeof an analyte sensor. This example shows a manufacturing phase, asterilization phase, a shipping/storage phase and an in vivo stage. Themonitored temperature and humidity experience by the sensor over thesephases is shown in FIGS. 12(a) and 12(b), respectively, and FIG. 12(c)shows the changes in the analyte sensor sensitivity that are determinedbased on these environmental parameters. The spikes in temperature thatare shown during the manufacturing stage arise during the curing of theanalyte sensor. It should be noted that the sensitivity may notappreciably change when the spikes are relatively small and/or short induration, and thus not all such spikes will require re-calibration ofthe analyte sensor. Other spikes that are larger in magnitude and/orduration do lead to sensitivity changes, thus indicating thatre-calibration may be required when the spikes exceed these thresholds.Accordingly, environmental parameters such as temperature and humiditymay only need to be monitored to determine if they exceed or fall belowcertain thresholds which have been shown to significantly affect thesensitivity.

It should be noted that all of the parameters mentioned above whichimpact the sensitivity of an analyte sensor and which are monitored atvarious points in time may also impact the baseline signal of thesensor. Accordingly, in addition to monitoring these parameters tocalibrate or otherwise adjust the sensitivity of the analyte sensor,these parameters may also be monitored to calibrate or otherwise adjustthe baseline of the analyte sensor. More generally, the monitoredparameters may be used to adjust any characteristic metric of theanalyte sensor and not just the sensitivity and/or the baseline.Examples of such characteristic metrics include, without limitation,long term drift, analyte sensor current, rate of exponential drift,ratio between fast and slow components in a dual-exponential sensitivitymodel, non-glucose baseline, compartmental bias between glucoseconcentration in local tissue surrounding the sensor and the bloodglucose, constant baseline, asymptotic magnitude of baseline rise due tomembrane degradation, asymptotic magnitude of baseline rise due tomembrane degradation, onset/transition time of baseline rise due tomembrane degradation, drift rate of baseline rise due to membranedegradation, initial magnitude of fast electrochemical break-in, driftrate of fast electrochemical break-in, initial magnitude of slowelectrochemical break-in, drift rate of slow electrochemical break-in.initial magnitude of compartmental bias, final magnitude ofcompartmental bias and drift rate of the disappearing compartmentalbias.

The previously mentioned complex adaptive calibration factor may bedetermined in part using predetermined statistical correlations thathave been identified between sensor behavior while in use and sensorbehavior that was measured during the various phases of the analytesensor lifecycle for a large number of previously deployed sensors. Thatis, instead of simply using pre-established environmental and/ormanufacturing parameters-to-analyte sensor sensitivity relationships tocalibrate a particular sensor, a relationship between one or more of thecharacteristic metrics of a large sampling of sensors that are measuredat various points in time and the resulting sensitivity or othercharacteristic metrics of the sensor samples during the use phase may beused to develop the complex adaptive calibration factor.

For example, FIG. 13 shows a sensor output signal obtained from a sensorduring steps of the manufacturing process, including at least one curestep, a membrane application step during which the sensor is coated in aparticular membrane, and a manufacture calibration measurement step todetermine an initial or in vitro value of analyte sensitivity, baseline,interferent sensitivity, impedance value, etc. As shown, the sensoroutput signal varies during each step. The shape of this signal over allor some of these steps defines a sensor signature that may be obtainedfor a large number of sensors during the manufacturing phase. Byexamining the behavior of these sensors during subsequent phases,particularly during the use phase, statistically useful correlations maybe found between the sensor signature and sensor behavior. In this wayby measuring the sensor signature of a particular sensor during thevarious steps of the manufacturing phase it may be possible to predictits behavior (e.g., one or more of the characteristic metrics) at alater time. For instance, it may be possible to obtain a predictedsensitivity profile of sensitivity change over time for a particularsensor.

While the preceding discussion has focused on the use of themanufacturing and/or environmental parameters monitored during variouslifecycle phases of an analyte sensor to facilitate calibration of thesensor, these monitored parameters may also be used for other purposesas well. For instance, based on the monitored parameters various actionsmay be taken by analyte monitoring system. For instance, if one or moreof the environmental parameters exceeds a specified threshold for acertain period of time, a message may be generated on a receiver (e.g.,a user's mobile communication device) indicating to the user, forinstance, that the expected end of life of the sensor has been reduced,or that the quality of the calibration is below some recommended valueor that that the confidence level in the sensor reliability is belowsome recommended value, or that the sensor is only suitable for certainoperating modes (e.g., sensor life, accuracy, insights, trends, analytevalue, alarms, monitoring type 1 diabetic patients but not type 2diabetic patients, or vice versa, or that the sensor is only suitablefor implantation at certain sites such as the abdomen or arm).Alternatively, or addition thereto, other actions that may be performedas a result of monitoring the environmental parameters may includeadjusting various startup parameters of the analyte monitoring system(e.g., requiring a longer than normal break-in period for the sensor),switching to an operating mode in which glucose levels are only reportedas being in a range (e.g. low, medium or high) instead of an operatingmode in which glucose concentrations are reported, initiating an in vivocalibration process, using a default calibration value, and using atemperature, humidity and/or complex compensated calibration value.

The reduction in errors that can be achieved by calibrating apreconnected system in comparison to a conventional (non-preconnected)system have been modeled by taking into account a subset of thevariables that affect the system. The model only uses a unitlessmeasurement of current (counts) and calibrates to a known glucosecalibration solution. The variation induced by component and measurementvariation are eliminated. In particular, the values of gain and offsetare not measured and are calibrated to a unit value so that theirinduced error is eliminated. The system has been calibrated with theexact components that influence the values of the contact resistance,leakage current and vias voltage. Therefore their variability iseliminated and they can be modeled as fixed values.

Hierarchical Models for Sensor Manufacturing Process, Sensor BenchCharacterization, and In Vivo Performance

In one variant of the subject matter described herein, statisticalprocesses may be employed as part of the closed-loop feedbackmanufacturing process.

It has been found that the sensor manufacturing process, sensor benchcharacterization, and in-vivo sensor properties are all loosely tiedwith each other. That is, while sensor process parameters may bemonitored to ensure they are within limits and each sensor is thenevaluated to determine if it meets the predefined criteria, processparameters and in vitro properties are not highly predictive of sensorin vivo properties (e.g., sensitivity). Typically, in vivo propertiesare estimated less from manufacturing process variables and more fromcalibration, since in a conventional system each sensor is generallycalibrated every 12 hours or so with a blood glucose meter.

When automatic calibration techniques are employed it may becomenecessary to rely more and more on sensor characteristics and less oncalibration through meters and the like. Thus, the mathematicalrelationship between manufacturing process variables, sensor in-vitro(or bench) characteristics and in-vivo properties becomes important. Inaddition, as the number of sensors being manufactured increases, thenecessary resources and time may not be available to exhaustively testeach sensor for pass/fail criteria or to estimate their in vitroproperties. A mathematical/statistical framework could thus provide analternative way for relating the sensor manufacturing process withsensor in vitro sensitivity and expected in vivo sensitivity. Ideally,process variables can be set to produce sensors with specificsensitivities.

There are a number of sensor process and design parameters that may beadjusted to build sensors with specific properties. These includerelative humidity, temperature, cure time, dip time, layer thicknesses,and raw material properties/proportions. The behavior of sensors invitro and in vivo depends on these process variables and may be modeledusing mathematical and statistical models. Hierarchical models are atype of multi-level statistical model where different random effectsthat impact processes and measurements are quantified in multiple levelsas conditional probabilities. For example, the variability of theprocess at specific set points may be modeled in level 1 (highestlevel), the variability of sensor behavior in vitro at level 2, andvariability of sensors in vivo at level 3. The model eventually mayrelate these levels so that variables from one level (e.g., second orthird) can be used to estimate variables in a different level (e.g.level 1).

One example of a framework for a hierarchical model for sensormanufacturing and in vivo properties is described below, where:

Xp represents process and design parameters, such as relative humidity,temperature, curing time, dip time, layer thickness, raw materialcharacteristics, etc.

Mp is a vector of target sensor properties defined by the processparameters (Xp)Mp=N(f(Xp),Σ²)

That is, sensor properties are a function of all of the sensor designand process parameters. The overall distribution of sensor propertieshas a mean of process set-point or target with a variance of Σ2. Notethat non-normal distributions are also possible.

The vector of sensor properties that are verified on the bench is:Mb=N(Mp,Γ ²)

For example, if a lot of 10,000 sensors is manufactured, 100 of them maybe sampled to estimate process properties. So the distribution of benchverified properties is normal with a mean target lot Mp and a varianceof Γ².Mi:N(g(Mb),V ²);

These are the actual in-vivo properties of the sensor. The distributionof the sensors is described by a function ‘g’ that translates thein-vitro properties into in-vivo. This is also referred to as in-vivo toin-vitro correlation. A simple example of the function ‘g’ is aproportionality constant from in vitro to in vivo. In a general case thefunction ‘g’ is a transformation from in vitro to in vivo with multiplefactors. In a matrix form this may be written as:Mi=G*Mb,

where G may have factors for sensitivity, drift, and baseline andinterdependencies.

$G = \begin{bmatrix}s & {s\_ d} & {s\_ b} \\{s\_ d} & d & 0 \\{s\_ b} & 0 & d\end{bmatrix}$where the diagonal terms s, d, and b are sensitivity, drift, andbaseline related in vitro to in vivo factors, while the off-diagonalterms are the cross-correlations between sensitivity and drift s_d, andsensitivity and baseline s_b. The elements of the matrix may betime-varying.

Once this model is developed there are various multiple applications inwhich it may be employed. In one application, process information may beincorporated into a continuous glucose monitor (CGM) algorithm (i.e., ajoint probability algorithm), thus enabling reduced and factorycalibration, as described in US-2014-0278189-A1, entitled “AdvancedCalibration for Analyte Sensors”, incorporated by reference in itsentirety. In another application, given that large scale sampling of themanufacturing process is cumbersome and expensive, this hierarchicalmodel may be used to estimate the process parameters and target sensorproperties through sampling of multiple lots from in vitro and in vivo.A third application involves the tracking of field performance anddirectly correlating it with manufacturing. The model may helpproactively track process parameters based on field data, allowingcorrective actions to be taken more rapidly.

Estimating Sensor Properties for Longitudinal Field Data

In another variant of the subject matter described herein, sensorproperties such as sensitivity may be estimated from field data. Forexample, predictive models may be created by mapping manufacturingparameters to in vivo sensor behavior from very large datasets (assumingthe sensors have unique sensor IDs to trace the field data to themanufacturing data).

Sensor sensitivity is typically estimated by comparing sensor current toreference glucose measurements. However, this becomes difficult orimpossible when field data either has no reference glucose measurementsfor comparison (i.e. for a factory calibrated product) or when referenceglucose measurements are unreliable (e.g. if the meter is of poor orunknown quality and reference measurements are not trustworthy). Thisproblem can be addressed as follows.

Individual users may have stable glucose dynamics across several weeksor months, assuming they are consistent with their therapy approach andtheir underlying physiology is not changing dramatically. As a result,observed differences in raw sensor signal statistics (e.g. mean,standard deviation, median, percentiles, skewness, etc.) from sensor tosensor may reveal differences in sensor properties such as sensitivity.Although sensitivity estimated in this way may not be as reliable assensitivity measured through comparison with accurate reference glucosemeasurements, with sufficiently large datasets the information may beuseful for detecting patterns in sensor behavior and be used toconstruct predictive models of field sensor behavior or detectunexpected shifts in field sensor behavior.

For example, when a wire is obtained from a new wire vendor isintroduced to production, it generally is not anticipated to have anyimpact on sensor sensitivity. However, field data shows that acrossthousands of users, standard deviations of raw sensor readings are,e.g., about 2% higher, in sensors from lots using the new wire vendorthan the historical standard deviations for each user. This patterncould trigger further investigation into the impact of the wire vendor,or the data could be incorporated into factory calibration models. Inthis way the predicted sensitivity in the factory calibration algorithmcan be adjusted to account for the impact of the wire vendor, leading toimproved accuracy.

NMR Method to Characterize Carbosil/PVP Ratio in Diffusion ResistanceLayer Solution

In yet another variant of the subject matter described herein, which maybe used to improve the accuracy of sensors during manufacturing, amethod may be employed to characterize the Carbosil/PVP ratio in thediffusion resistance layer of the sensor.

The diffusion resistance layer is one the most important layers in theCGM membrane of the sensor, which provides stable, predicable glucoseand oxygen permeation and blocks some interference agents. Currently,certain sensors use a Carbosil 2090A and PVP (K90) blend system.Carbosil dissolves in THF but is not able to be dissolved in ethanol.However, PVP can be dissolved in ethanol but not in THF. So the currentdiffusion resistance layer solutions are prepared by using a THF/Ethanolmixed solvent to dissolve both Carbosil and PVP.

Sensor performance is related to the Carbosil/PVP ratio (e.g., a highPVP will yield high sensitivity). In particular, the uniformity of thedip coating will be affected by Carbosil/PVP ratio. Also, the diffusionresistance layer dipping solution viscosity will be affected byCarbosil/PVP ratio change. Overall, sensor stability will be affected byCarbosil/PVP ratio.

In order to make reproducible sensors, a consistent, accurateCarbosil/PVP ratio in the RL dipping solution is an important parameterto control. However, up to now, no method has been developed to evaluatethe Carbosil/PVP ratio in an RL solution. Thus, in order to improvesensor accuracy and thus enhance the ability of automatic calibration,it is important to track the quality of each RL dipping solution beforethe sensors are dipped.

In one aspect, nuclear magnetic resonance (NMR) spectroscopy is used todetermine the Carbosil/PVP ratio in the diffusion resistance layersolution. In particular, proton NMR technology may be employed.

One particular example of a process that may be employed to determinethe Carbosil/PVP ratio is described by the following steps:

1. Prepare sample

-   -   1.1 C2090A/PVP THF/EtOH solution with 22 wt. % EtOH, 13.6 wt. %        of PVP.    -   1.2 Cast a film using RL solution and dry at 50 C overnight till        a consistent weight achieved. Remove solvent.    -   1.3 Cut a piece of thin film and dissolve in DMSO-d6 with        concentration of 10 mg/mL. (20 mg/mL, 50 mg/mL)

2. Run proton NMR and obtain FID signal followed by a baselinecorrection, tune phase and obtain spectra.

3. Integration of MDI peaks in Carbosil; calculate integration number ofeach proton.

4. Integration of H2 peaks in PVP; calculate integration number of eachproton.

5. Calculate mole ratio Carbosil and PVP

6. Obtain calibration curve of Carbosil/PVP blend.

7. Calculate Carbosil/PVP wt. %/wt. % ratio based upon calibrationcurve.

FIG. 14 shows the NMR spectrum of PVP in DMSO-d6. FIG. 15 shows the HNMRspectrum of Carbosil in DMSO. FIG. 16 shows the HNMR spectrum of an RLfilm (Carbosil/PVP blend with removal of solvent). The MDI peak inCarbosil and the H2 peak in PVP were selected to calculate theCarbosil/PVP ratio.

An HNMR Calibration process was conducted to validate the method. First,an RL solution with different Carbosil/PVP ratio with a predeterminedCarbosil/PVP ratio was prepared as shown in the Table shown in FIG. 17 .Then, H NMR was run using DMSO-d6 as the solvent. FIG. 18 shows theresulting HNMR calibration curve.

Temperature and Humidity Sensing During Storage

As discussed above, impedance measurements of the analyte sensor may beobtained during the shipping and storage phases to monitor humidity fora sensor preconnected to electronics. In addition, the temperaturesensor in the transmitter could record the temperature and thus thetemperature and humidity sensors could indicate if the analyte sensorwas outside its recommended humidity and temperature during shipping andstorage. In addition, an algorithm could be created to compensate theinitial factory calibration parameters based on the temperature andhumidity conditions and the duration of exposure. (It should be notedthat the initial factory calibration may be performed on a single sensorusing a single bath or on a lot or brick of sensors e.g., 30 sensors,which can be simultaneously calibrated using a single large bath).

In one variant, measuring current alone may be sufficient to indicatehumidity or extreme humidity. Some embodiments of the sensor system maywake up periodically and perform a measurement to identify when thesensor has a signal to indicate a system start up (due to hydrationafter deployment). A fully preconnected sensor would also measurecurrent when only humidity is present. Accordingly, it would be a usefulindicator indicating that the analyte sensor was exposed to humidityconditions during shipping and storage. If the system is not fullyintegrated with the electronics, a removable adhesive tab (e.g., a“sticky tab”) could be placed on the transmitter's electrodes, whichwould conduct current when humidity is present. This would allow thetransmitter to measure humidity. The tab would be removed beforetransmitter use.

In another variant the sensor storage conditions may be determined usingresistors or other materials that have a known response to temperature,humidity, or a combination of both, and which generate an electricalsignature (e.g., resistance, current). In addition to the circuit thatcauses a transmitter to be activated when it detects a sensor, the samecircuit or a separate circuit could be arranged to be triggered wheneverthe temperature and/or humidity exceeds a threshold. Based on theduration of the trigger and the magnitude of the measurement (reflectiveof the temperature and/or humidity), the system would be able to adjustthe calibration factor to better predict in-vivo performance byinferring changes due to environmental conditions. In one particularimplementation, a strip of the environmentally-sensitive material may beplaced across the transmitter electrodes so that it only allows currentto pass under specific environmental conditions. In some cases thisserves as an irreversible circuit or material change that is onlytriggered above a threshold, creating an on/off indicator to shift thepredicted sensor response to a new performance bin or to prevent use ofthe product if extreme conditions were reached.

In yet another variant, the packaging in which the sensor is stored mayinclude a temperature and/or humidity sensitive material that changescolor based on the temperature and/or humidity so that a color changewould indicate the storage conditions experienced by the sensor. Forinstance, in one example the material may be located on the packageinterior in the form of a small region (e.g., a dot). The color of thematerial may be detected by a camera or other detector in the mobiledevice in which the system app is located, which can determine thedegree of color change. Alternatively, the color change could bedetected directly by the transmitter or other sensor electronics, whichas noted above, can be used to better predict in-vivo performance byinferring changes due to environmental conditions.

In yet another variant, the calibration parameters that are used by thecalibration algorithm may differ from sensor to sensor based on thesensor manufacturing details and other factors. From the transmitterpoint of view, the user inserts a sensor and enters the “sensor id” intothe display and based on this, the display will either send the actualset of parameters that needs to be used or sends a code that causes oneof a predefined set of parameters to be used. To achieve this, thetransmitter may store multiple sets of parameters. If the set ofparameters is large, storing multiple copies of the parameters mayoccupy too much storage space.

To address this problem, in some cases only one default set ofcalibration parameters may be stored on the transmitter and, to obtainan updated set, only the differences between the default set and theupdated set need to be sent. Since usually the differences are going tobe small, this may be more efficient. This approach also provides theflexibility to change any individual parameter. That is, the set ofparameters does not have to be fixed and they can change during thefactory calibration process. If the set of parameters is an orderedlist, then the changes can be specified as a list of paired values suchas (parameter number, new value).

Calibration Code Encoding

In yet another aspect, a sensor calibration code or some other codeassigned to the sensor in the factory may be linked to the customer'saccount in the following manner. In this example the transmitter that isshipped with the sensor is assumed to be re-useable and ships withenough sensors to cover the duration of the transmitter's life(generally determined by its battery). For instance, a transmitter thatis usable for three months would need 6 sensors that last 14 days each.In such a system a factory calibration code associated with the sensormay be communicated to the user's mobile device using the followingmethod.

First, at step 1 the customer orders a package of sensors, possiblyusing a dedicated app on their mobile device. At step 2, while in thefactory the package and the sensors to be included therein are scannedto establish a link between the packaging and the sensors. At shipping(step 3), a shipping label with the customer's account information isscanned along with the packaging to thereby create a link between thecustomer's credentials and the packaging. This link is stored by themanufacturer in a cloud server or the like for future reference.

At step 4 the package with the sensor is shipped to the customer. Atstep 5 the customer inserts a new sensor and installs the newtransmitter and the package initiates a session. After the userinitiates the session, at step 6, the sensor code information stored inthe cloud server or the like can be retrieved since the package ofsensors and the transmitter has been previously linked to the customer'saccount.

Enhancements to Closed Loop Manufacturing Feedback Process

In another variant, additional information may be used to supplement theinformation that is available concerning the manufacturing process,which is stored by a sensor that is preconnected to sensor electronics.For example, the manufacturing process typically involves a sequence ofsteps that are performed at different stations in the factory. Inprinciple the amount of time needed by the operator to perform any givenstep should be about the same for each part or component that is beingassembled or process being performed at that station. Any significantlyhigh variability in these times may indicate an immature station or aprocess where the operator has to excessively make adjustments to partsand fixtures during assembly, which could highlight areas for processimprovements. In some cases a small device may be placed at eachworkstation to perform a study of the time needed to perform theactivity required at that station. The device may include an actuator(e.g., button, motion sensor, light sensor) that provides a simple,unintrusive means by which the operator can quickly interact to allow amicrocontroller in the device to record the time for each deviceinteraction. The operator would be instructed to interact with thedevice every time they complete the task at their station. The devicethen stores the times, which can be subsequently output for analysis.

Initial Calibration

In another variant, when ethylene oxide (ETO) sterilization is employed(instead of e-beam sterilization) the initial drift profiles for some ofthe conditions are found to be very flat (see the graph of FIG. 19 ,where group 4 (left) is an ETO condition and group 6 (right) is theunsterile condition using the same timescale with about 12 sensor driftprofiles for each group). ETO processing may thus be used to stabilizethe sensor against high humidity storage or shipping excursions.

In another variant, sensors may undergo ETO sterilization with arechargeable desiccant present in the packaging during the ETO process.The desiccant may then be “baked out” after ETO to recharge itsdesiccating capability. After sterilization in ETO, an additionaldesiccant may be added to the final sensor packaging and/or the finalpackaging may employ a moisture barrier to minimize humidity. Severalsensors may be sterilized in this manner using bulk packaging thatcontains the sensors and the desiccant.

Communication of Sensor Parameters Via NFC

In yet another variant, sensor information (e.g., sensor parameters,calibration factors or codes, environmental characteristics) of the typedescribed herein that is to be communicated may be sent from the sensorto the transmitter via an NFC protocol. In one embodiment, this may beaccomplished by providing the sensor base or interposer with an NFC tagand providing an NFC reader on the receiver (e.g., a user mobiledevice). The sensor information received by the receiver from the sensorbase or interposer can be subsequently communicated to the transmitterat e.g., system startup.

Electronic Hardware Correction

Factory calibration correction techniques for continuous analytemonitoring systems have typically employed digital techniques forstoring and adjusting for sensor lot variability. In some embodiments itis useful to use analog electronic circuitry to modify the sensorsignal. Using a resistor with a known value can serve to modify ananalog signal and change the amount of current or the measured voltage.In one example the resistor may be combined as part of a gain circuitwith an operational amplifier to tune the gain on the output signal. Theresistor can be selected from a variety of known resistance values orconfigured through a process (e.g. laser trimmed resistor).

Factors Influencing Sensitivity and Impedance

A nonlimiting set of factors that have been found to influence theimpedance and/or sensitivity of a preconnected analyte sensor is shownin the Table of FIG. [IFD1675]. These factors, which are directed toselect manufacturing and storage conditions, can be measured forindividual sensors and/or sensor lots and correlated with sensitivityand impedance measurements at different times during the sensorlifetime. In this way the values of these factors may be usedindividually and/or in combination with one another to determine therelationship between the measured impedance and sensitivity at any timeduring the sensor lifetime, thereby allowing adjustments to thecalibration factor that is used to calibrate the sensor at any timeduring its operational life.

Updating of Slope Parameters on a Regular Basis

Currently, a “cal check” procedure is performed in the factory in whicha sensor undergoes in vitro calibration to obtain a slope value. Thisvalue is used to seed a joint probability algorithm with initial andfinal sensitivity values using linear transformations. That is:Mean Initial Slope=calcheck*mstart+bstartMean Final Slope=calcheck*mfinal+bfinal

Deviations from this linear relationship can be taken into account byupdating the mean of the initial and final slope using a linearcombination of parameters measured at the factory (e.g. during calcheck) and parameters measured in real time. For instance, the equationfor the final slope can be revised as:Mean FinalSlope=a*calcheck+b*meanSensorCurrent+c*sigmaSensorCurrent+d*sensorCv+e*calcheck+. . . +Offsetwhere the real time parameters include the mean sensor current(meanSensorCurrent), the standard deviation of the sensor current(signaSensorCurrent) and the coefficient of variation of the sensorcurrent (sensorCv). Other real time parameters that may be included inthe mean final slope include mean sensor current, the root mean squareof the sensor current, and the sensor current taken at a specifiedpercentile within the distribution of sensor current values. A similarapproach can be taken to adjust the mean of the starting sensitivity. Byusing a combination of factory and real-time measurements in this way,the performance of the system can be improved because the linearcombinations allows factory information to be linked with in-vivo sensormeasurements. The parameters that describe the equation below may besuch that the final slope estimate may be updated periodically (e.g.,every day) during sensor wear.Retrospective Calibration of CGM Signal

Retrospectively calibrating the CGM signal with or without the use ofSMBG is important for the professional CGM market and other use casessuch as technical support and for benchmarking the performance offactory calibrated CGM. With retrospective calibration, there is anopportunity to remove certain artifacts that corrupt the real-time CGMsignal, such as time-lag, transient faults, compression, noise, and datagaps. As described below, in some embodiments data gaps, noise andartifacts in the CGM signal can be removed using prediction algorithms.This approach generally works best after the removal of time lag fromthe signal and smoothing.

It is commonly believed that glucose levels can be predicted reasonablywell about 30 minutes into future. The accuracy of the predicted signaldrops as the prediction horizon goes beyond 30 to 40 minutes. Thus, anysignal artifacts or data gaps that are shorter than 30-40 minutes can bereplaced with a predicted signal without losing key information need forclinical use. Further, given the retrospective use, any errors in thepredicted glucose level may be removed by the analysis of data. Someways that can be accomplished are as follows:

-   -   1. Identify the area(s) of artifacts in the signal.    -   2. Replace the artifact signal with a predicted glucose level.    -   3. Evaluate the difference between the predicted glucose level        at a final point in time and an initial point in time of the        post artifact signal.    -   4. Correct the predicted signal by feeding back this error into        the prediction. For example, if there is an error of 30 mg/dL        between the final predicted CGM and the initial time point of        post-artifact CGM, this error can be distributed evenly (or        using a weighted average) over the duration of the predicted        signal. This way, the predicted signal is corrected to result in        a smooth correction of the artifact, without discontinuities.

In another embodiment, prediction can be used bidirectionally, toincrease the duration of the artifacts that can be corrected. Thefollowing describes how longer duration artifacts may be corrected:

-   -   1. Identify the beginning and end of artifacts that need        removal/replacement.    -   2. Create two CGM time series signals, the first time series        being the normal signal (time moving forward from the beginning        to the end of session) and the second time series being in        reverse time (from the end of the session to the beginning).    -   3. Use the prediction to replace artifacts on both the forward        and reverse time series. i.e., each artifact will have two        possible replacements, one based on the forward time series        signal and one based on the reverse time series signal.    -   4. Pick the midway point between the two replaced artifacts.        These should correspond to the same time point in the CGM        signal. Depending on how variable the glucose signal is during        this period, the two signals may be meet at the midway point or        be different at the midway point.    -   5. Given that the prediction is reasonably accurate for short        durations, the best estimate of the glucose level at the midway        point is the mean of the values from the two time series.    -   6. Now, the error between the mean and the actual midpoint        values from the time series can be fed back into the predicted        artifact replacements to correct them.    -   7. The corrections can be weighted depending on the quality of        the signal before or after the artifact.

This approach for the correction of artifacts makes the signal morereliable and increases the duration of artifacts that can becorrect/removed.

Replacement Sensors

Sensors sometimes fail before their marketed duration (e.g. 7 days). Insome cases the sensor electronics (e.g., a transmitter) can be packagedwith a 3 month supply of sensors in a single box (e.g. a 6 pack). In onevariant, the transmitter can then be coded with a single common sensorcode. If one of the sensors in that sensor box fails and a replacementsensor needs to be sent to the customer, the transmitter can send thesensor code to the dedicated app on the customer's mobile device. Thecustomer can then ask for a replacement sensor through the app. The appcan then relay the sensor code to the manufacturer or the like, who cansend the customer the appropriate sensor with the correct code thatmatches the transmitter that was included in the original sensor box.

Configurable Calibration Frequency

In one variant, the frequency at which a transmitter issues acalibration request to the dedicated app on the customer's mobile devicecan be configurable. In one example, the transmitter can have a defaultcalibration frequency (e.g., one calibration per day, two calibrationson day one followed by one calibration per day thereafter, etc.) if ithas not been supplied with pre-existing calibration information. Inanother example the transmitter may or may not issue calibrationrequests to the dedicated app based on the availability of thepre-existing calibration information. Moreover, the calibrationfrequency may be based on the type of app being run on the mobiledevice. The transmitter may also store different default calibrationfrequencies based on the type of app being used.

Transfer of Calibration Data to the Transmitter

In another variant, a method for transferring calibration coefficientsto the transmitter or other sensor electronics from a disposable sensorwithout user intervention may operate as follows. This method employs amemory embedded in the sensor which transfers the calibrationcoefficients and/or other information to the transmitter. Theinformation that may be transferred could include, for example, a lotnumber, expiration dates, and the authentication information that couldallow the manufacturer to assure that genuine sensors are being used.Such authentication information may operate in accordance withcryptographic and other algorithms such as hashes (e.g., SHA-256) and/ormay operate in accordance with standards such as the Federal InformationProcessing Standards.

While the information may be transmitted from the sensor to thetransmitter using any suitable connector or wirelessly using, forinstance, RFID, these are not always appropriate for low cost,environmentally robust systems and may require significant developmentor tooling changes. Instead, the following technique may be employed totransfer calibration codes and/or other information.

Without loss of generality, this technique will be described as beingapplicable to a sensor that uses a low bias voltage (e.g., less than 1volt) and has at least 2 electrical connectors (e.g., a reference andworking electrode). The sensor is assumed to be wired or otherwiseconfigured with a memory element in which the information is stored. Thememory element uses a single wire for power and signal and is connectedto the sensor's working electrode. A ground connection is connected tothe sensor's reference electrode.

To initiate a session, the transmitter periodically checks for thepresence of a new sensor by waking up from sleep mode, enabling the biasvoltage and looking for a predetermined response from the sensor. If theresponse indicates the presence of a new sensor, the transmitter willtransition into an operating mode as described below. If the response isnot as expected, indicating no sensor present, the transmitter will goback to sleep for a predetermined period.

If a predetermined signal indicates the presence of a new sensor, thetransmitter will attempt to recover calibration coefficients and otherinformation from the memory device. The memory device is configured toonly respond to signal pulses if they are above a predetermined voltagelevel, above the nominal operating bias voltage of the sensor. Thememory device is both powered and communicates using the same pin. Thememory device may operate in an active mode, where it incorporates ashort term charge storage device (such as a capacitor) to power thememory chip while it signals back to the transmitter while thetransmitter places its pin connected to the memory element in highimpedance. Alternatively, the memory device may operate in a passivemode and present a high or low load to the transmitter in order tosignal the appropriate information back to the transmitter, using thetransmitter as the master clock.

The time needed to communicate the relevant calibration coefficients andany other information (such as expiration date, serial number) isgenerally short relative to the lifetime of the sensor, and the highervoltages used during communication for such a short time will not damageany enzymes used in the sensor. Hence the short term overpotential doesnot affect the long term operation of the sensor, and may even help withsensor electrochemical break in. Once the memory device has passed therequired information and the transmitter ramps down to the nominalsensor bias voltage, it either may enter a very high impedance state soas not to falsely elevate the observed signal current from the sensor,or it may be designed to draw a known current which can be subtractedfrom the sensor signal, or it may do a combination of both.

In some embodiments, the transmitter may signal to the memory elementthe end of life of the sensor, which will place an indicator or the likein its internal memory indicating that the sensor is expired. This canprevent accidental reuse of the sensor since the memory element willcommunicate to the transmitter that it has already been used, even if itis disconnected and reconnected. At this point the duration of thecommunication session and the value of the applied voltage is lesscrucial since it does not matter if the enzyme is damaged, since thesensor has already reached its end of life.

In an alternative embodiment, when initiating a session, thetransmitter, upon waking up, may simply interrogate the connections forthe presence of a memory device, and later check that the sensor isoperating normally once the calibration data has been successfullytransferred.

Calibration of EGV in a Closed-Loop System

In closed-loop systems (e.g., artificial pancreas systems), updatingestimated glucose values (EGVs) as a result of a calibration can lead toincorrect dosing because when a calibration happens, the EGVs are likelyto change more than natural glucose levels change. When such changes inEGVs are input to artificial pancreas algorithms, they may lead them toincorrectly predict EGVs. Current artificial pancreas algorithms acceptcalibration updates and update the EGVs after the calibration iscompleted.

This problem can be addressed in some embodiments by updating a few EGVdata points prior to calibration as well as after calibration for use bythe artificial pancreas algorithms. In this way the algorithms cancapture the correct EGV changes.

Use of Biometric Data in Preventing Incorrect Entry of Calibration Data

Manual entry of calibration data or other reference information can beprone to error. One way to detect and prevent the use of incorrectlyentered data may use biometric data of the user. Such information may beavailable to the dedicated app on the user's mobile device, either fromsensors incorporated in the mobile device or from third party devicesthat are able to provide the biometric data to the mobile device. If thecalibration or other data that is entered is found to be incompatible orinconsistent with the biometric data, the app can present an errormessage or take other appropriate action. As a simple example, if a 35year old male is found from a biometric sensor to have a heart rate of170 bpm and the CGM shows a glucose reading of 40 mg/dl, this isindicative that the glucose reading is in error.

Efficient Storage of Calibration Coefficients and Other Parameters

The transmitter needs to store relevant calibration coefficients and/orother parameters for different sensors. When a sensor is inserted, theuser typically enters the sensor ID into the dedicated app and the appin turn sends either the parameters to the transmitter or an identifierthat corresponds to a predefined set of parameters already stored in orotherwise available to the transmitter. In any case, the transmitter mayneed to store multiple sets of parameters. However, if the set or setsof parameters are large, there may not be sufficient memory available tothe transmitter to store all the necessary parameters.

In one variant, the transmitter may store a limited number (e.g., one)of sets of parameters that can serve as default sets of parameters.Then, when a newer set of parameters are to be used, only thedifferences between the values in the default set of parameters and thenew set of parameters need to be stored in the transmitter. Since thedifferences are usually going to be small, this can be a more efficientway to store the data. This also provides the flexibility to change anyparameters since the parameters established during factory calibrationdo not have to remain fixed. In one embodiment, the default set ofparameters can be provided as an ordered list and the changes can beprovided as a list of paired values that specify the parameter numberand the value of the difference from the default value.

Exemplary Sensor System Configurations

Embodiments of the present invention are described above and below withreference to flowchart illustrations of methods, apparatus, and computerprogram products. It will be understood that each block of the flowchartillustrations, and combinations of blocks in the flowchartillustrations, can be implemented by execution of computer programinstructions. These computer program instructions may be loaded onto acomputer or other programmable data processing apparatus (such as acontroller, microcontroller, microprocessor or the like) in a sensorelectronics system to produce a machine, such that the instructionswhich execute on the computer or other programmable data processingapparatus create instructions for implementing the functions specifiedin the flowchart block or blocks. These computer program instructionsmay also be stored in a computer-readable memory that can direct acomputer or other programmable data processing apparatus to function ina particular manner, such that the instructions stored in thecomputer-readable memory produce an article of manufacture includinginstructions which implement the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer implemented process suchthat the instructions which execute on the computer or otherprogrammable apparatus provide steps for implementing the functionsspecified in the flowchart block or blocks presented herein.

In some embodiments, a sensor system is provided for continuousmeasurement of an analyte (e.g., glucose) in a host that includes: acontinuous analyte sensor configured to continuously measure aconcentration of the analyte in the host and a sensor electronics modulephysically connected to the continuous analyte sensor during sensor use.In one embodiment, the sensor electronics module includes electronicsconfigured to process a data stream associated with an analyteconcentration measured by the continuous analyte sensor in order toprocess the sensor data and generate displayable sensor information thatincludes raw sensor data, transformed sensor data, and/or any othersensor data, for example. The sensor electronics module can includeelectronics configured to process a data stream associated with ananalyte concentration measured by the continuous analyte sensor in orderto process the sensor data that may include raw sensor data, algorithmprocessed, transformed sensor data, and/or any other sensor data, forexample. The sensor electronics module can include a processor andcomputer-readable program instructions to implement the processesdiscussed herein, including the functions specified in the flowchartblock or blocks presented herein.

In some embodiments, a receiver, which can also be referred to as adisplay device, is in communication with the sensor electronics module(e.g., via wired or wireless communication). The receiver can be anapplication-specific portable device, or a general purpose device, suchas a P.C., smart phone, tablet computer, smart watch, wearable display,haptic device and the like. In one embodiment, a receiver can be in datacommunication with the sensor electronics module for receiving sensordata, such as raw and/or processed data, and include a processing modulefor processing and/or display the received data. The receiver can alsoinclude an input module configured to receive input, such as calibrationcodes, reference analyte values, and any other information discussedherein, from a user via an input method (e.g. keyboard ortouch-sensitive display screen), and can also be configured to receiveinformation from external devices, such as insulin pumps, insulin pens,wearable sensors, connected devices, accelerometers, and referencemeters, via wired or wireless data communication. The input can beprocessed alone or in combination with information received from thesensor electronics module. The receiver's processing module can includea processor and computer program instructions to implement any of theprocesses discussed herein, including the functions specified in theflowchart block or blocks presented herein.

While the disclosure has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive. Thedisclosure is not limited to the disclosed embodiments. Variations tothe disclosed embodiments can be understood and effected by thoseskilled in the art in practicing the claimed disclosure, from a study ofthe drawings, the disclosure and the appended claims.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit, and each intervening value between the upper and lowerlimit of the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

What is claimed is:
 1. A method for self-calibration of an analytesensor system that includes an analyte sensor operatively coupled tosensor electronics, comprising: taking a first measurement at a firsttime during a first life phase by applying a bias voltage with thesensor electronics to the analyte sensor to generate sensor data, theanalyte sensor system having an initial characteristic metric determinedat the first time, when the analyte sensor is operatively connected toone or more components of the sensor electronics, wherein the first lifephase comprises manufacturing, and the initial characteristic metric isbased on one or more manufacturing and/or environmental parametersrelated to the first life phase; storing, in the sensor electronics,information associated with the first life phase of the analyte sensorand information associated with a second life phase, the second lifephase comprising one or more of shipping, storage, pre in vivo, orsensor session; taking a second measurement at a second time during thesecond life phase subsequent to the first time to determine a change tothe initial characteristic metric of the analyte sensor system based onone or more parameters, wherein the one or more parameters impact theanalyte sensor sensitivity during the first and second life phases; andusing the sensor electronics to automatically calibrate, without userintervention, the analyte sensor system based at least in part on thedetermined change to the initial characteristic metric.
 2. A method forself-calibrating an analyte sensor system that includes an analytesensor operatively coupled to sensor electronics, comprising: applying abias voltage with the sensor electronics to the analyte sensor togenerate sensor data, the analyte sensor system having an initialcalibration factor that is used to convert sensor data to analyteconcentration values; storing, in the sensor electronics, informationdescribing a plurality of life phases of the analyte sensor and aplurality of times, the plurality of times comprising a first timecorresponding to a first life phase of the plurality of life phases anda second time corresponding to a second life phase of the plurality oflife phases, wherein one or more parameters impact the analyte sensorsensitivity during each of the plurality of life phases, wherein thefirst life phase comprises manufacturing, and the second life phasecomprises one of shipping, storage, pre in vivo, or sensor session;using the sensor electronics to update the initial calibration factor ofthe analyte sensor system at the plurality of times based at least inpart on the one or more parameters that are monitored during one or moreof the plurality of life phases; and using the sensor electronics toautomatically calibrate, without user intervention, the analyte sensorsystem based at least in part on the updated calibration factor.
 3. Themethod of claim 2, wherein using the sensor electronics to update theinitial calibration factor of the analyte sensor system includesdetermining a complex adaptive calibration value that is based at leastin part on manufacturing conditions and environmental conditionsexperienced by the analyte sensor during one or more of the plurality oflife phases of the analyte sensor.
 4. The method of claim 2, wherein theone or more manufacturing parameters include process parameters anddesign parameters, wherein the process parameters include temperature,humidity, curing time, and dip time, and wherein the design parametersinclude analyte sensor membrane thickness and raw materialcharacteristics.
 5. The method of claim 2, wherein the one or moremanufacturing parameters include process parameters, the processparameters including temperature, humidity, curing, time and dip time.6. The method of claim 2, wherein the one or more manufacturingparameters include design parameters, the design parameters includinganalyte sensor membrane thickness and raw material characteristics. 7.The method of claim 2, further comprising using the sensor electronicsto receive remotely stored sensor performance data to update the initialcalibration factor.
 8. The method of claim 7, wherein the remotelystored sensor performance data that is received concerns analyte sensorsthat have experienced or been exposed to manufacturing and/orenvironmental parameters that are most similar to one or more of themonitored manufacturing and/or environmental parameters.
 9. A method forself-calibration of an analyte sensor system that includes an analytesensor operatively couplable to sensor electronics, comprising:operatively coupling at a first time the analyte sensor to one or morecomponents of the sensor electronics to define a packagable analytesensor arrangement, the packagable sensor arrangement having an initialsensitivity metric determined subsequent to the first time, wherein thefirst time correspond to a manufacturing life stage; identifying asecond time subsequent to the first time, the second time being selectedto correspond to a life phase of the analyte sensor, wherein the lifephase comprises one of shipping, storage, pre in vivo, and sensorsession; applying an analyte interrogation signal with the one or morecomponents of the sensor electronics to the analyte sensor at the secondtime; measuring a signal response to the analyte interrogation signal;based at least in part on the measured signal response and the lifephase of the analyte sensor, determining a second sensitivity metric;and automatically calibrating, without user intervention, the packagablesensor arrangement based at least in part on the initial sensitivitymetric and the second sensitivity metric.
 10. The method of claim 9,wherein the analyte sensor is continuously operatively coupled to theone or more components of the sensor electronics between the first andsecond times without interruption.
 11. The method of claim 9, whereinmeasuring the signal response includes measuring an impedance of thepackagable analyte sensor arrangement.
 12. The method of claim 9,wherein automatically calibrating the packagable sensor arrangement isbased on an established relationship between an impedance of thepackagable analyte sensor arrangement and analyte sensor sensitivity.13. The method of claim 9, wherein automatically calibrating thepackagable sensor arrangement includes automatically calibrating thepackagable sensor arrangement in vivo.
 14. A method for performing anaction with an analyte sensor system that includes an analyte sensoroperatively coupled to sensor electronics, comprising: applying a biasvoltage with the sensor electronics to the analyte sensor to generatesensor data, the analyte sensor system having an initial characteristicmetric determined at a first time when the analyte sensor is operativelyconnected to one or more components of the sensor electronics, the firsttime within a manufacturing life stage; storing, in the sensorelectronics, information associated with a plurality of life phases ofthe analyte sensor, the plurality of life phases comprisingmanufacturing, shipping, storage, pre in vivo, and sensor session;identifying a second time subsequent to the first time, the second timebeing selected to correspond to one of the plurality of life phases ofthe analyte sensor, the second time within the shipping, storage, pre invivo, or sensor session life phase; using the sensor electronics at thesecond time to determine a change to the initial characteristic metricof the analyte sensor system based at least in part on one or moremanufacturing and/or environmental parameters; and based at least inpart on the determined change to the initial characteristic metric,performing an action selected from the group comprising: generating amessage, initiating a re-calibration process, using a defaultcalibration value and using a temperature and/or humidity compensatedcalibration value.
 15. The method of claim 14, wherein generating themessage includes generating an error message.
 16. The method of claim14, wherein generating the message includes generating a messagerequesting a manual recalibration.